{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **1 导入检查数据**\n",
    "    * 1.1 导入数据\n",
    "    * 1.2 异常值检测\n",
    "    * 1.3 训练集与测试集合并\n",
    "    * 1.4 检查null值和缺失值  \n",
    "* **2 特征分析** \n",
    "    * 2.1 数值型\n",
    "    * 2.2 分类型值\n",
    "* **3 填充缺失值** \n",
    "    * 3.1 Age \n",
    "* **4 特征工程** \n",
    "    * 4.1 名字和称谓  \n",
    "    * 4.2 家庭人数\n",
    "    * 4.3 船舱等级\n",
    "    * 4.4 船票\n",
    "* **5 建模** \n",
    "    * 5.1 简单的模型\n",
    "    * 5.2 叠加模型 \n",
    "    * 5.3 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "\n",
    "from collections import Counter \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, \\\n",
    "                                GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, \\\n",
    "                                    cross_val_score, StratifiedKFold,\\\n",
    "                                    learning_curve\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 加载并检查数据\n",
    "### 1.1 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')\n",
    "IDtest = test['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 异常值检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 合并训练和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_len = len(train)\n",
    "df = pd.concat(objs=[train, test], axis=0).reset_index(drop=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 检查null值和缺失值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.特征分析\n",
    "### 2.1 数值型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GpLp+7gG+o2L79mnxuLHHGHPYGPM8cAfOLn5P1PbJlVLK6yzi+VK99UBHEWkrIkE4k/KikwuJSGcgEvih3LZIEQl2PY7BOb1iVW3fHvO0H3ZXnDfNXAVk4pyM9/7aPrlSSnmbty46GmNsIjIZ+Apnt765xphtIjIV2GCMOZG8r8fZVFy+uaQr8JqIOHBWjGeU711yujxtw34LZ3V/zIlqvlJKNUTevHHGGPMF8MVJ2544af0pN8d9D/T0WiAuNSZsV1/E3caY2d5+cqWU8jaxnsG3phtj7CISLSJBxpji+ghKKaVOmx+OwucpjycwANaIyCKgdBwRY8xzdRKVUkqdJm/d6dgQeZqwU12LBWhWd+EopVQtnek1bGPM03UdiFJKecMZP1qfiHyLmzt8jDEjvR6RUkrVRs03xPgtT5tEHij3OASYgPP+eKWUalDO6F4iAMaYjSdtWiMiOkWYUqrh0SYRiSq3agH6Agl1EpFSStWCP06u6ylPm0Q2UtaGbQN+B26ri4CUUqpWztRufSLSDzhgjGnrWr8ZZ/v173hhIBOllPK6RlzDrunMXgOKAURkGPAP4B0gl6rHkFVKKZ8Rq9Xjxd/U1CRiNcZkuR5fi3M2ho+Aj0RkU92GppRSp+EM7tZnFZEAY4wNGEXFqXQ8bf/2mov6WeiYZKHEDp+usXEoq3KZFlFwxZAAAqyQfNDBl+sdFfYP7mbhwr5WnplfQsHxegq8Cme/Pp24i0dQnJ7JynMudVum26zHiBs7HHthEZtve5i8n50tUUk3jafjI3cCkPyPVzg4r9azD3nN2p+2MPvNeTgcDsaNHsFNE9yf27ffr+PxmS/wxsyn6dKhHbl5+UyZ+QI7du3hovPP475JN9dz5NW7Z1J7Bp0bTdFxO9Nn7+S33UcrlXn2qZ5ERwVhtQqbt+Xy3KvJOBxw/pAY/nRDG1q3bMKf7/+JnbsqH+sLOzav4rN3Z+Bw2Blw/gRGXvbnCvt3/7qBRfNmcGj/b9z4l5n0GnBh6b7sI6kseP1JcjIPg8DEB18lKjapvk+hsjO4l8gHwAoROQIUAqsARKQDzmaRetMxSYgOF57/1EbLGGHcACuvf2mvVG7cQCuLfrCTcsTwh1FWOiQKu1Kd10vDm0D7RCHnaFWz/NSvlHc+5veX36P33Gfc7o8dO4ywDm34rusYIgb0oseLT/H9kGsIjGxOpymTWT1wAsYYzvvxY9I+/wZbTl49n0FldruD5+a8w6ynHiIuOoqJDz7B0P59aNuq4hu5oLCQhUuW0q1T+9JtQUGBTLx+Anv3p7Bnf0p9h16tgedG0SqxCdfdvo7unZvxwJ0dmfRA5WlOH39mOwWFzv/Lvz/SjfOHxLJ8VQZ79hXw6PRtPHhXp/oOvUoOh51P3prGpEdep3l0PLOnXEu3PueT0LJDaZnImBZce8c0Vix+u9LxH7zyKKPHT6JTz8EcLzrmyQwu9aKhxFEXqj0zY8w0nBMVvA0MLTdAtwX4S92GVlGXVsKm3c7acsoRQ0iQ0DS0YpmmoRAcKKQccYa5abeDrmeVfdqO7Wdl6UZ7lZOy1bes1Rsoyar6cy/+slEcfM9Zc875cTOBzcMJTogldsxQMpavoSQ7F1tOHhnL1xB34Xn1FXa1fk3eTcsW8SQlxBEYGMDooQNZve7kbvzw+vsfccP4SwgKDCzdFhoSQq9unQkKCqxU3tfOGxjN/745DMC2nfk0DQsgOjKoUrkTydpqFQIDLJx4x+xLKeDAwYY1v+T+XVuJjm9FdHwrAgKC6D3oYrZt/LZCmajYJBLP6lzpdu/DKbtw2G106jkYgOCQMIKCT3pD+or3ZpxpcGr8KDLGrDXGfAI0E5HLRORSIM8Y81Pdh1emWRMhr6BsPa/AEN6k4h88vImQV2DKlXEeB9C5pZBfYEjLrpdwvSIkMZ7ClMOl60UHDxOSFE9IYjxFB8ptT0kjJDHeFyFWkpGVTVxMWbf92OgoMjIr/tF/2/M76UcyGdKv1lPc1ZuY6GDSj5S1oaVnHicmunLCBnj26Z4sfm8QBYU2vvs+o75CPGW52WlERLcoXY+Iiic3K82jY48c2kdoWDhvz7qH5x6ZwOf/+RcOR+VvvD4hFs8XP+NRxCIyEVgHXIlzmrC1IvKnugysUgxutlWckacKBgKtMKynhW82OWou34C4GybSGOO2n6lHf4t64C6O8ufhcDh4fu5/mHzrDfUZVq25rYtV8Se//8mtXP7HHwgMtNDn7Mi6DKt23MTv6dCkdoeNvTs2cukND3DP3+eTlX6A9SsayHUUq9Xzxc94euHwr8A5xphMABGJBr4H5rorLCKTcF2gvOSWlzn3/ImnFVz/zhb6dHR+pqRmGsKblO0LbyLkn/QN8+Rad3gTyC80RDaDiKbCnZcGlG6/fVwAry+xcbTotEKrF4UHDxPaMoET9dOQpASOp6ZTdPAwUcP7l5YLaRlP1op1vgnyJHHRUaQfKbsanJGZRUxUROl6QWERe/en8Jcp0wHIysnloemzeObRe+nSoV29x1udKy9O5NILnTXQX5PziYsJLt0XFx3Mkayq5/MoLjGs/jGT8wZEs2FTw/xa1zwqnpzMQ6XrOVlphEfGeXRsRFQCiW26Eh3vnFS8R99R7Nu1GedtGj7mhzVnT3l6ZilAfrn1fOBAVYWNMXOMMX2NMX1PN1kDrNvp4NXFNl5dbOPX/Q56t3eG2zJGKCoxHD0pYR8tdL5RWsY4k3bv9hZ2HDCk58DMBTb+/bFzySuA1xY37GQNkP75NyT9YTwAEQN6YcvL5/jhDDKWriZ29FACIsIJiAgndvRQMpau9nG0Tl06tuPAocOkpqVTUmLj69VrGdKvT+n+pmFNWPLuKyycM4uFc2bRrVP7BpmsAT7+IpVb79nIrfdsZNXaI4wd6RyNoXvnZhwtsJGZXTFhh4ZYStu1rRYY1DeKfSkFlX5vQ9GqfQ+OHN5PZnoKNlsxm374gu7nnu/xsYXHcjma5/xwTt72I/FJ7Ws4qp404jZsT2vYB4EfReQznF+kLgfWich9UD8zzyQfNHRKMtxzRQAlNvj0+7L2sjvGBfDqYufggYt/dDB+sJXAAGe3vuSDDaOpwJ3e854lenh/gmIiGbl3BclTX0ACnS/J/jkfkv7lCmIvGs6IHcuwFxayZeKjAJRk55I8/WWG/rAQgORpL1GSXa+ddqoUYLVy35//yH1Pz8ThcHDJqGG0O6slb7z/EV06tGVo/z7VHn/VpHs5VliIzWZj1bqNPPfkQ5V6mPjCDxuyGNQ3ivlz+pd26zvhrdnncus9GwkJsTLj8e4EBliwWoWNm3P47EvnnNXDBkbz/27vSETzQGY+0ZPkvUe5/8mtvjodAKzWAK645TFenzEJ43DQb8QVJLTswP8WvECrdt3pfu5I9u/eyjuz7qHgWB7bf/qOpQtf4q8zF2GxWLn0xr/y2rTbMBhatu3GgJFX+fR8SjXiGrZ40vYpIk9Wt7+6CQ6efLek4WbM09T/th6+DqFO9N88z9cheN0VDzWsnhne8tBTQ3wdQp249NyAWld7ixa95HHOCbnsLr+qZp/yjDMiEgnkmIZylUsppco7U8cSEZEnRKSL63GwiHwD7AbSRGR0fQSolFKnxGL1fPEzNX0UXQucaKy72VU+FhgOTK/DuJRS6vRYLJ4vfqamJpHick0fFwIfGGPswK8iUu9jiSilVI0a8XjYNX3EHBeRHiISC5wPLC23r0kVxyillO804jsda6ol3wMsxNkMMssYsxdARC4GKo98o5RSvtaIa9jVJmxjzI9AFzfbvwC+qKuglFLqdBk/vOXcU56OJRItIs+LyE8islFEZrtuT1dKqYalETeJeBrxh0AGzoECrnI9nl9XQSml1GlrxAnb054eUcaYv5Vb/7uIjK+LgJRSqjZMI27D9vQj5lsRuU5ELK7lGmBJXQamlFKnxYs1bBEZKyI7RWSXiDzsZv8tIpIhIptcy8Ry+24WkWTX4pX57qqtYYtIPs7BngS4Dzgx2IQVOApUO8aIUkrVOy/VsEXECrwEXIBzxNL1IrLIGLP9pKLzjTGTTzo2Cmd+7Iszh250HVursXZr6iXSrDa/XCml6psXe4n0B3YZY/YAiMiHOEcqPTlhu3MhsMwYk+U6dhkwFuc8uaetphp2F2PMDhFxOyZmfU8TppRSNTqFi4nlJ1txmWOMmeN6nETFcf9TgAFufs0EERkG/Abca4w5UMWxtR4nuKaLjvfhPJlny20rP0rfyNoGoJRS3mROIWG7kvOcKna7nZnwpPXPcQ7ZcVxE7gDewZkXPTn2lNV0Zm+ISIIx5nxjzPk4Z08/CvyCs3ufUko1LCKeL9VLAVqVW28JpJYvYIzJNMacmJ35deBcT489HTUl7FeBYgBXlf8fOD9Bcqn6U0kppXzGiMXjpQbrgY4i0lZEgoDrgEXlC4hIi3KrlwG/uh5/BYwRkUjXHAJjXNtqpaYmEeuJRnOcQ63OMcZ8BHwkIps8eYI2rQJrE1+D1BhnZgFY1+smX4fgdUn3LK25kB8KD6l6AmD/5oVBQL00zrUxxiYik3EmWisw1xizTUSmAhuMMYuAu0XkMsAGZAG3uI7NEpG/4Uz6AFPL5dLTVmPCFpEAY4wNGEXFxnkdXlUp1eB488YZd+MmGWOeKPf4EeCRKo6dC8z1WjDUnHQ/AFaIyBGgEFgFICIdcDaLKKVUw+KHt5x7qqZ+2NNEZDnQAlhabjIDC/CXug5OKaVOlXHbQaNxqLFZwxiz1s223+omHKWUqp1T6dbnb7QdWinVuGjCVkop/+Dww9nQPaUJWynVuDTi4VU1YSulGhVtw1ZKKT9xRvcSUUopf6I1bKWU8hfahq2UUv7BIdpLRCml/II2iSillJ/Qi45KKeUntIatlFJ+wpvDqzY0mrCVUo2KXnRsAPZsW8nX/52Gw+Gg15CrGTR2UoX9tpJiFr/9IIf3byM0LILLJ84iIqYldnsJX86bQtr+7TgcNnoMHM+gsbf76CwqWvvTFma/OQ+Hw8G40SO4acKlbst9+/06Hp/5Am/MfJouHdqRm5fPlJkvsGPXHi46/zzum3RzPUdetbNfn07cxSMoTs9k5Tnuz6fbrMeIGzsce2ERm297mLyftwOQdNN4Oj5yJwDJ/3iFg/M+rbe4PXHL5ZGc0zWU48WGV+Znsvdg5Vlfrh0bwbC+YTQNtXDzY2WTZsdEWrnjmmjCw6wcLXTw4vtHyMq112f4bv3y0xrmz52Jw+Fg6OjxXHTlnyrsX7ZoHqu//gSLNYBm4ZHcfNeTRMclcmDvTv7z2jQKC49hsVi5eMJt9Bt6oY/OoqLG3IbtF409DoedpR9M5ZrJb/DnJ5ewff1ijqTuqlBmy5oFhDQJ546/LaPfqFv47pN/AbBj4/+w24q57YnPueXRj/l55XxyjqT44CwqstsdPDfnHf71+F957/ln+Hr1D+w9cLBSuYLCQhYuWUq3Tu1LtwUFBTLx+gncdfP19RmyR1Le+Zh14yZWuT927DDCOrThu65j2Hrn4/R48SkAAiOb02nKZNYMuYbVg6+m05TJBESE11PUNevdJYSE2EDumZHK6wszuW1ClNtyP20v4LHZhyttv2lcJCs3HuPB5w7x0bJcrr84oq5DrpHDbuf912dw95QXeXr2R6xf9T9SD+yuUKZV2y48OvM/PDnrv/QZNIqP3p0NQFBwCLfe/Teenv0R9zz+IvPn/ouCY/m+OI1KvDinY4PjFxEf+n0LkXGtiYhthTUgiG79LiF5y/IKZZK3fEPPQVcA0KXPhezb8QPGGESE4uOFOOw2bMVFWAMCCQ5t6ovTqODX5N20bBFPUkIcgYEBjB46kNXrNlYq9/r7H3HD+EsICiybGzM0JIRe3ToTFNTw5svMWr2BkqyqJyOKv2wUB99z1pxzftxMYPNwghNiiR0zlIzlayjJzsWWk0fG8jXEXXhefYVdo37dm7Byw1EAkvcXExZiIaJZ5a/eyfuLycmvXHNOig/kl+QiALbtKqJv9yZ1G7AH9u76hbgWrYhNaElAYCD9hl7I5nXfVSjTpWc/goNDAWjX6WyyM9MAiE9sTXxiawAiouIIbx5Jfm6tpyz0CoN4vPg3LF/+AAAgAElEQVQbjxO2iCSJyGARGXZiqcvAysvPTqNZZELperOIePKz0yqWyUmjWaRzAmOLNYDg0GYUHsumc58LCQoO5YWHhvLyo+cz4II/ERrm+9pNRlY2cTFltbTY6CgyMrMrlPltz++kH8lkSL9z6ju8OhOSGE9hSlkNtOjgYUKS4glJjKfoQLntKWmEJMb7IkS3IptbycwpS8SZuTaimnveVrovtYQBPZ1Jun+PUJqEWGjaxLf1pZzMdKKiy/7GEdHxZGdlVFl+9fJP6dFnSKXte5N/wWazEZvQqk7iPFVnfA1bRJ4B1gBTgL+6lgeqKT9JRDaIyIbvFs/xQpim8qaTrwSbymUE4dDeLYhYmPzMKu74+3LWfT2XnIwDlcrWN+Mu3nLn5HA4eH7uf5h86w31GVadEzdX8I0xbm8ndvc38hV3HQ9OJbz3FmfTrX0IM+5tQdf2IWTm2LA7fHt+7p69qjrn2hVL2LdrO2PGV7xekpOVwdzZU7hl8lNYLA0jATbmGranFx3HA52NMcc9KWyMmQPMAXjrW7f/F6ekWWQC+dllta/8nDSaRcS5KXOI8MgEHHYbxwvzCQmLYPv6xbTrfh5WayBh4dEkte/DoX1biYj1bW0gLjqK9CNlXyEzMrOIiSqr+RcUFrF3fwp/mTIdgKycXB6aPotnHr2XLh3a1Xu83lJ48DChLRM48V0iJCmB46npFB08TNTw/qXlQlrGk7VinW+CdBkzuCmjBjQDYPeB40RHlNWoo5sHkJ3n+UXD7Dw7z77jrL0GBwkDejahsMi3CTsyOo6szLJvqjmZaURExVYqt33zWr5Y+CYP/O0NAgODSrcXFhzlhWl3c/kNd9Gu89n1ErMnHP7R0ntaPD2zPYDPGkxbtO5JVvrv5Bw5gN1WzPb1S+hw9sgKZTqcPZKtP3wCwI6fvqJ154GICOFRLdi380eMMRQfLyB1z2aiE3yf8Lp0bMeBQ4dJTUunpMTG16vXMqRfn9L9TcOasOTdV1g4ZxYL58yiW6f2fp+sAdI//4akP4wHIGJAL2x5+Rw/nEHG0tXEjh5KQEQ4ARHhxI4eSsbS1T6Nden3R3lo1iEemnWI9dsKGdbXee2j41lBFBQ53LZVV6VZE0tpLX38yOZ8u/5oXYR8Stp06E76of0cSTuIraSE9au/ole/ERXK7N+zg/dencZdj8wiPKKsCc9WUsIrz9zPoBHj6Dv4gnqOvHoGi8eLv6m2hi0iL+D85lQAbHLNoF5ayzbG3F234TlZrAGMufYJ5j8/EeOwc/bgCcQmdmTlotm0aN2Djr1G0WvIVXz+1l959fELCG3SnMsnzgKgz/AbWfLuI7w5dRzGGM4efCVxLbvUR9jVCrBaue/Pf+S+p51dqi4ZNYx2Z7Xkjfc/okuHtgzt36fa46+adC/HCgux2WysWreR5558iLatkuop+qr1nvcs0cP7ExQTyci9K0ie+gIS6Pw32z/nQ9K/XEHsRcMZsWMZ9sJCtkx8FICS7FySp7/M0B8WApA87SVKsqu+eFnffv61kHO6hDL74USKS5zd+k545t4WPDTrEAA3XhLBkHPCCAoUXp6SxDfrjrJwaS7dOoRw/UURGGDHniLe/Nj3F+is1gCun/gQ/576fzgcDoaMupzEs9rz2Qcv07p9N3r3H8HCd2dxvKiA1/71IABRMQlMfnQ2G75fym/bf+Jofg7ff7sIgFv/MpVWbTv78pSAxt2tT6prJxSRajv4GmPeqekJvNEk0tCMi/ftV/W6sq7XTb4OwevevWepr0OoE/93a+Wmi8ZgePcmtc62O3cf8DjndG7fyq+ye7U17BMJWUTCgCJjjN21bgWC6z48pZQ6NY25hu1pI85yILTceijwtffDUUqp2tFeIhBijCm9SmKMOSoivu/5r5RSJ3EY/7uY6ClPz+yYiJReBRORc4HCuglJKaVOn9aw4R5ggYikutZbANfWTUhKKXX6/DERe6rGhC0iFiAI6AJ0xnkz1A5jTEkdx6aUUqfMmDM4YRtjHCLyrDFmEPBLPcSklFKnzdGIa9ietmEvFZEJ4m4gCKWUakAcxuLx4m88jfg+YAFwXETyRCRfRPLqMC6llDot3rzoKCJjRWSniOwSkYfd7L9PRLaLyBYRWS4ircvts4vIJteyyBvn5tFFR2NMM288mVJK1TVvtWG7bhB8CbgASAHWi8giY8z2csV+BvoaYwpE5E7gn5R1yCg0xvT2SjAuHk8RJiKRQEcg5MQ2Y8xKbwajlFK15cVeIv2BXcaYPQAi8iFwOVCasI0x35Yrvxb4g7ee3B1Px8OeCKwEvgKedv18qu7CUkqp02OMeLyUH7vftZSfLDYJKD94foprW1VuA74stx7i+p1rRWS8N87tVPph9wPWGmPOF5EuOBO3Uko1KI5TKFt+7H433FXV3Q4sJSJ/APoCw8ttPssYkyoi7YBvRGSrMWa3u+M95WnCLjLGFIkIIhJsjNkhIr4fR1EppU7ixd4fKUD5mU5aAqknFxKR0cBjwPDyk7wYY1JdP/eIyHfAOUCtEranZ5YiIhHAp8AyEfnMXeBKKeVrp9IkUoP1QEcRaSsiQcB1QIXeHiJyDvAacJkxJr3c9kgRCXY9jgGGUK7t+3R52kvkCtfDp0TkW6A58L/aPrlSSnmbty46GmNsIjIZ5zU7KzDXGLNNRKYCG4wxi4CZQFOcQ3cA7DfGXAZ0BV4TEQfOivGMk3qXnJaaJjAIAe4AOgBbgTeNMbZTeYIHXilodBMYrP1iva9DqBNJndv4OgSv++PsMb4OoU5kLd/p6xDqxE3Dap9tV2475nHOGdY9zK9uBqyphv0OUAKsAi4CuuG8AKmUUg3SmTz4UzdjTE8AEXkTaJxzYymlGo0zefCn0hH5XO05dRyOUkrVjv0MTti9yo0ZIkCoa10AY4wJr9PolFLqFJ2xNWxjjLW+AlFKKW+oph+F3/N4LBGllPIHZ/JFR6WU8isOrWErpZR/cDi0hq2UUn6hMU8RpglbKdWo6EVHpZTyE2dstz6llPI3etFRKaX8hDaJKKWUnziTb01XSim/ojVspZTyE5qwlVLKTzi0SaRhuHxIIF1bWym2wfxvjnPwSOWP0qQY4bqRwQQGwK/77Hy2xjlCbGK0MGF4EAFWweEwfLyqhAPppzK/ct24Z1J7Bp0bTdFxO9Nn7+S33UcrlXn2qZ5ERwVhtQqbt+Xy3KvJOBxw/pAY/nRDG1q3bMKf7/+JnbsqH+srt1weyTldQzlebHhlfiZ7DxZXKnPt2AiG9Q2jaaiFmx87ULo9JtLKHddEEx5m5WihgxffP0JWrr0+w6/k7NenE3fxCIrTM1l5zqVuy3Sb9RhxY4djLyxi820Pk/ezc0aopJvG0/GROwFI/scrHJz3ab3FXZPdv6zkqw+nYRwOep93NUMumlRh/77f1rNs/nTSUnZy5aTn6Hru2NJ9m7//hNVLXgFg6CV30mvwFTQEjbmG7bXphetal7MsxEZYmPF+EQtXFDNhWJDbchOGBbFwRTEz3i8iNsJCl7Ocp3jJoCCWbShh1oIivlpfwriBgfUZvlsDz42iVWITrrt9HTNf+o0H7uzottzjz2znlrs3ctNdG4hoHsj5Q2IB2LOvgEenb2Pzttz6DLtGvbuEkBAbyD0zUnl9YSa3TYhyW+6n7QU8Nvtwpe03jYtk5cZjPPjcIT5alsv1F0fUdcg1SnnnY9aNm1jl/tixwwjr0Ibvuo5h652P0+PFpwAIjGxOpymTWTPkGlYPvppOUyYTENEwRiV2OOx8+f5Urr/nDe6YuoRt6xaTkbqrQpnmUS249NZ/0KP/uArbC4/lsOrzF/nTo//lT48uYNXnL1J4rGH8H9odni/+xm8Sdvc2VjbsdE4nuT/NQUiw0KxJxTLNmkBIkLAvzflKbNhpo3sb1wixBoIDnV+VQoKE3AYw1eR5A6P53zfOhLVtZz5NwwKIjqz8QVRQ6KxdWq1CYICltAaxL6WAAwcL6y1eT/Xr3oSVG5y1/eT9xYSFWIhoVnmk3uT9xeTkV645J8UH8ktyEQDbdhXRt3uTSmXqW9bqDZRkVZ2Q4i8bxcH3nDXnnB83E9g8nOCEWGLHDCVj+RpKsnOx5eSRsXwNcReeV19hVyt17xaiYlsTGdsKa0AQ3ftdwm+bllcoExHTkviWXRCpmCp2/7Katt2GEBoWQWhYc9p2G8LuX1bVZ/hV8uKs6Q2O3yTs5mEWco6WJdnco4bmYZbKZcrNv5l7rKzMZ2uKGTcokCk3hXDpoEC+XFuCr8VEB5N+5HjpenrmcWKi3X9zePbpnix+bxAFhTa++z6jvkI8LZHNrWTmlCXizFwbUc09H1p9X2oJA3o6k3T/HqE0CbHQtEnD/lcNSYynMKXs20LRwcOEJMUTkhhP0YFy21PSCEmM90WIleTnpBEelVC63iwynvycNM+PjSw7NvwUjq1rxni++Jsa3wUiEi8ib4rIl671biJyW92HdnIclbedPON7dZ+Xg7oHsOj7Ev4+r4hF35dw9fnuE2N9chtvFf9E9z+5lcv/+AOBgRb6nB1Zl2HVmvvXyvPj31ucTbf2Icy4twVd24eQmWPD3sBvX3M3fZ4xxu0f4+T/W19xH4entc7KxzaUKQQdxvPF33hy0fFt4C3gMdf6b8B84M2qDhCRScAkgAtueIGzh/7ptIIb3D2AAd2cIR5IdxDRtOwfonlTIe+kZo2cYw4iys1a3zxMyD3mbB7p2zmAz9Y4mw8277Zz9QjfJOwrL07k0gtbAPBrcj5xMcGl++KigzmSVfni3AnFJYbVP2Zy3oBoNmzKrvNYT8WYwU0ZNaAZALsPHCc6oqxGHd08gOw8zy8aZufZefYd57eI4CBhQM8mFBY17HdX4cHDhLZM4MSrEpKUwPHUdIoOHiZqeP/SciEt48la0TDmsg6PTCAvq6z2n5+dRrOIOI+ObRaRwL7fys4jLzuN1p36V3NE/Wkgn4d1wpPvmTHGmP8CDnBOxgtU++4zxswxxvQ1xvQ93WQN8P02G7MWFDFrQRHb9tro29mZvM+Kt1B03JBfULF8fgEcLzGcFe88rb6dA9j2uzPUvAJD+0Tn9g5JFo7k+uZV/fiLVG69ZyO33rORVWuPMHak82tl987NOFpgIzO7YsIODbGUtmtbLTCobxT7Ugoq/V5fW/r9UR6adYiHZh1i/bZChvVtCkDHs4IoKHK4bauuSrMmltKK6fiRzfl2fcPp/VKV9M+/IekP4wGIGNALW14+xw9nkLF0NbGjhxIQEU5ARDixo4eSsXS1j6N1SmzTk6z038nOOIDdVsy29Uvo1GukR8e27zGUPdtWU3gsl8JjuezZtpr2PYbWccSeacxNIp7UsI+JSDSu70AiMhCo98vBv+530KW1g4dvCKHEBvO/LUts914dwqwFzotUH60s4bqRQQRYYed+Ozv2O2vYC74rZvzQICwCNrthwXfH3T5PffphQxaD+kYxf07/0m59J7w1+1xuvWcjISFWZjzencAAC1arsHFzDp99mQrAsIHR/L/bOxLRPJCZT/Qkee9R7n9yq69Op9TPvxZyTpdQZj+cSHGJs1vfCc/c24KHZh0C4MZLIhhyThhBgcLLU5L4Zt1RFi7NpVuHEK6/KAID7NhTxJsfZ/noTMr0nvcs0cP7ExQTyci9K0ie+gIS6Hz77J/zIelfriD2ouGM2LEMe2EhWyY+CkBJdi7J019m6A8LAUie9hIl2Q2jN4XFGsDYG57gg39PxGHs9B4ygdikjnz32WwSW/egU+9RpO7dwoKXJ1NUkEfylm9Z8dkL3DF1CaFhEZw37v+YO+0qAM679C5Cw3zfmwf8s/eHp6Sm9jQR6QO8APQAfgFigauMMVs8eYIHXmkA3TG8bO0X630dQp1I6tzG1yF43R9nj/F1CHUia/nOmgv5oZuG1X72gdeWVnUlqLLbx/jXbAc11rCNMT+JyHCgM84rEjuNMb7vYqGUUm74Y1OHp2pM2CJy5UmbOolILrDVGJNeN2EppdTpOaMTNnAbMAj41rU+AliLM3FPNcbMq6PYlFLqlPljdz1PeZKwHUBXY0waOPtlA68AA4CVgCZspVSDcWr93P2qCdujhN3mRLJ2SQc6GWOyRETbspVSDYrdt+OE1SlPEvYqEVkMLHCtTwBWikgYkFNnkSml1GlozG3Yntw4cxfOOx17u5Z1gDHGHDPGnF+XwSml1Kny5q3pIjJWRHaKyC4RedjN/mARme/a/6OItCm37xHX9p0icqE3zq3GhG2cDUK7gRLgCmAU8Ks3nlwppbzNW3c6iogVeAm4COgGXC8i3U4qdhuQbYzpAMwCnnEd2w24DugOjAVedv2+WqmySUREOrme8HogE+f4IaK1aqVUQ2ZOqZtItRcd+wO7jDF7AETkQ+ByYHu5MpcDT7keLwReFOcoWJcDHxpjjgN7RWSX6/f9cArBVVJdG/YOYBVwqTFmlyvge2vzZEopVde8eGt6EnCg3HoKzt5xbssYY2yue1SiXdvXnnRsUm0Dqq5JZAJwGPhWRF4XkVH4Wx8YpdQZx+EwHi8iMklENpRbys+R5i7fnVx9r6qMJ8eesipr2MaYT4BPXL1BxgP3AvEi8grwiTFmaW2fXCmlvO1UeokYY+YAc6rYnQK0KrfeEkitokyKiAQAzYEsD489ZZ5cdDxmjPmPMWac60k3AZWuliqlVEPgxeFV1wMdRaStiAThvKa36KQyi4CbXY+vAr5xddRYBFzn6kXSFuiIs4ddrZzSrOnGmCzgNdeilFINjsNLHbFdbdKTga8AKzDXGLNNRKYCG4wxi3BO5DLPdVExC2dSx1XuvzgvUNqAu4wxtb6l55QStlJKNXTGi+NhG2O+AL44adsT5R4XAVdXcew0YJr3otGErZRqZOz2xnuroyZspVSj0lAmOa4LdZ6wrx1Z9aSy/mp4/yG+DqFOhIc0vtdq/2WNc2aWqFGdfR1C3Sip/et1pg+vqpRSfuPU7nT0L5qwlVKNSiNuEdGErZRqXBxaw1ZKKf/g0F4iSinlH7x140xDpAlbKdWoaLc+pZTyE9qGrZRSfqIRV7A1YSulGhe7F2cwaGg0YSulGhW9cUYppfyEJmyllPITjThfa8JWSjUuWsNWSik/of2wlVLKT2gvEaWU8hPaJKKUUn5CE7aPbd74A/PeeA6H3cGIMZdx2VU3V9j/xafv892yz7BaAmjWPIJJd08hJq4FAEcyDvPGC9PIOpIOAn99Yhax8Ym+OI1KdmxexWfvzsDhsDPg/AmMvOzPFfbv/nUDi+bN4ND+37jxLzPpNeDC0n3ZR1JZ8PqT5GQeBoGJD75KVGxSfZ+CW7/8tIb5c2ficDgYOno8F135pwr7ly2ax+qvP8FiDaBZeCQ33/Uk0XGJHNi7k/+8No3CwmNYLFYunnAb/YZeWMWz1K/dv6zkqw+nYRwOep93NUMumlRh/77f1rNs/nTSUnZy5aTn6Hru2NJ9m7//hNVLXgFg6CV30mvwFfUae1XOfn06cRePoDg9k5XnXOq2TLdZjxE3djj2wiI23/YweT9vByDppvF0fOROAJL/8QoH531ab3HXRAd/8iGH3c47r83k4akvEBUdxxP338K5/c8j6ax2pWXatOvE3557h+DgEL7+4iM+ePtF/vKgc7LiV2c9zeVX30LPcwZQVFiAWCy+OpUKHA47n7w1jUmPvE7z6HhmT7mWbn3OJ6Flh9IykTEtuPaOaaxY/Hal4z945VFGj59Ep56DOV50DJEGcl52O++/PoN7n3yFyOh4pj94I736DSexVfvSMq3aduHRmf8hODiU7/73Xz56dzaTHniGoOAQbr37b8QntiYnK52/P3Aj3c8ZTJOwZj48I+dr9eX7U7nx3rcIj4znzWlX0anXSGITy16r5lEtuPTWf7D2q7kVji08lsOqz1/ktikfAcKbf7+STr1GEhrWvJ7PorKUdz7m95ffo/fcZ9zujx07jLAObfiu6xgiBvSix4tP8f2QawiMbE6nKZNZPXACxhjO+/Fj0j7/BltOXj2fgXuNuYbdMN7l1didvJ34Fi2JS0giIDCQgeddwMYfV1Yo0+3svgQHhwDQoXMPZ20aOLh/Dw67jZ7nDAAgJLRJaTlf279rK9HxrYiOb0VAQBC9B13Mto3fVigTFZtE4lmdEYtU2H44ZRcOu41OPQcDEBwSRlBwaL3FXp29u34hrkUrYhNaEhAYSL+hF7J53XcVynTp2Y9gV7ztOp1NdmYaAPGJrYlPbA1ARFQc4c0jyc/Nqtf43Undu4Wo2NZExrbCGhBE936X8Num5RXKRMS0JL5ll0ofnLt/WU3bbkMIDYsgNKw5bbsNYfcvq+oz/Cplrd5ASVZulfvjLxvFwfecNeecHzcT2Dyc4IRYYscMJWP5Gkqyc7Hl5JGxfA1xF55XX2HXyBjj8eJvPKphi4gANwLtjDFTReQsIMEYs65OowOyM9OJiokvXY+KiWP3zm1Vll+xbBG9zh0EwKHUAzQJa8a/pz9ERloq3Xv347o/3oXFaq3rsGuUm51GRHSL0vWIqHj27dri0bFHDu0jNCyct2fdQ1Z6Ch17DOKS6+/FYvH9eeVkphMVXfZ6RUTHszf5lyrLr17+KT36VJ7UeG/yL9hsNmITWtVJnKciPyeN8KiE0vVmkfGk7vXstcrPSSM8suzY8Mh48nPSvB5jXQhJjKcw5XDpetHBw4QkxROSGE/RgXLbU9IISYx39yt8wm5rvL1EPK1hvwwMAq53recDL1VVWEQmicgGEdnwyfy3axWg2w9BETcbYfW3X7Jn169ccuUfAHDYbezcvokb/nQ3U597i4zDB1m5fEmt4vEaN+clVZzXyewOG3t3bOTSGx7gnr/PJyv9AOtXNIw2RLcvVxVl165Ywr5d2xkzvuI1iZysDObOnsItk5/C0gCasNzXxDx7rdz9RTx9nX3NXZzGGLfvv4ZUW23MNWxP3w0DjDF3AUUAxphsIKiqwsaYOcaYvsaYvldce0utAoyKiSPrSFmNJOtIOpFRMZXK/bJpHYsWvM19U/5FYKAztKjoOFq360xcQhJWawDnDhzO73t21Coeb2keFU9O5qHS9ZysNMIj4zw6NiIqgcQ2XYmOb4XVGkCPvqM4+Pv2ugr1lERGx5GVWfZ65WSmEREVW6nc9s1r+WLhm9z1yL9LXy+AwoKjvDDtbi6/4S7adT67XmKuSXhkAnlZZTXK/Ow0mkV49lo1i0ggL7vs2LzsNJo29+xYXys8eJjQlmXfDkKSEjiemu6sabcqt71lPMcPpfsiRLeMw+Hx4m88TdglImLFVV0QkVigXs62XceuHE49QPrhVGwlJaxdtYw+A4ZVKPP77p3MfXkG902ZSfOIqHLHdqPgaB55udkAbNuygaRWbesj7Bq1at+DI4f3k5megs1WzKYfvqD7ued7fGzhsVyO5jnbd5O3/Uh8UvsajqofbTp0J/3Qfo6kHcRWUsL61V/Rq9+ICmX279nBe69O465HZhFe7vWylZTwyjP3M2jEOPoOvqCeI69aYpueZKX/TnbGAey2YratX0KnXiM9OrZ9j6Hs2baawmO5FB7LZc+21bTvMbSOI/aO9M+/IekP4wGIGNALW14+xw9nkLF0NbGjhxIQEU5ARDixo4eSsXS1j6Mt43AYjxd/I558LRCRG4FrgT7AO8BVwBRjzIKajl2/M6fWf5VNG9bw3huzcDgcDB99KZdfcysL//MabTt05dwBw/jH45M58PsuIlw17+jYBO6f8i8Atv78I+/PfR6DoW37Ltx21yMEBAbWKp7DR5vW9pQA+PXnlXw2bwbG4aDfiCsYPf52/rfgBVq16073c0eyf/dW3pl1DwXH8ggMDKJZ8xj+OnMRAL9t/Z7P35uJwdCybTeumvgUAQFVfunxSHhIsRfOCrZuXMX8uf/C4XAwZNTlXHLVRD774GVat+9G7/4jeO6p2zm4bxfNI52vV1RMApMfnc3aFUt4+8WnSGxV1gPo1r9MpVXbzqcdy/7MJrU+H4BdW1ew9MPpOIyd3kMmMPSSO/nus9kktu5Bp96jSN27hQUvT6aoII+AwGDCwmO4Y6qz+W3T6oWs+eI1AIZccge9h0yodTxRo07/b3JC73nPEj28P0ExkRxPyyR56gtIoPOy1v45HwLQ/fkniB1zHvbCQrZMfJTcjc7rES1vmUCHh24HYNeMV0l55+NaxwNwScnOWrcXXXP/7x7nnP8+28Y/2qdcPErYACLSBRiFs/FuuTHmV0+O80bCbmi8lbAbGm8l7IbEWwm7ofFGwm6IvJGwr7pnj8c5Z+Hsdn6VsGvsJSLOfkpbjDE9gIbRAKyUUlVwGP9rm/ZUjQnbGOMQkc0icpYxZn99BKWUUqerMd844+mdji2AbSKyDjh2YqMx5rI6iUoppU6TJmx4uk6jUEopL/HH/tWe8ihhG2NW1HUgSinlDY566l8tIlHAfKAN8DtwjeselfJlegOvAOGAHZhmjJnv2vc2MBw4MT7ALcaYTdU9p0f9sEVkoIisF5GjIlIsInYRaRgjvSilVDkOu93jpZYextljriOw3LV+sgLgj8aY7sBY4N8iElFu/1+NMb1dS7XJGjy/ceZFnLelJwOhwETXNqWUalCMw3i81NLlOO9LwfVzfKVYjPnNGJPsepwKpAOVb/31kMcDNRhjdgFWY4zdGPMWMOJ0n1QpperKqSTs8uMeuZZJNT9DqXhjzCEA189qxxwQkf44h/TYXW7zNBHZIiKzRCS4pif09KJjgYgEAZtE5J/AISDMw2OVUqrenEo/bGPMHGBOVftF5Gsgwc2ux04lJhFpAcwDbjamNMBHgMM4k/gc4CFganW/x9OEfRPO2vhk4F6gFVD7+2uVUsrLvNmtzxgzuqp9IpImIi2MMYdcCdntCFgiEg4swTmcx/R6XvIAAAeiSURBVNpyv/vE6G/HReQt4IGa4qk2YZ+4WcYYs8+1qQjt4qeUasDqcRS+RcDNwAzXz89OLuBqmfgEePfksZfKJXvB2f5d9cDxLjW1YZcOsiwiH9UYvlJK+Vg99hKZAVwgIsnABa51RKSviLzhKnMNMAy4RUQ2uZbern3/EZGtwFYgBvh7TU9YU5NI+YFR2lVZSimlGoj6GjbVGJOJc0C8k7dvwNmTDmPMe8B7VRzv2Ri95dSUsE0Vj5VSqkHyx4kJPFVTwu7lukFGgNByN8sIYIwx4XUanVJKnaIzdiwRY4zvZ3VVSqlTYM7k4VWVUsqfOGy1vpjYYHk844w/EJFJro7wjUpjPK/GeE7QOM+rMZ6Tv/L41nQ/cSq3lfqTxnhejfGcoHGeV2M8J7/U2BK2Uko1WpqwlVLKTzS2hN1Y29ka43k1xnOCxnlejfGc/FKjuuiolPr/7Z1djFVXFYC/DxgDhDoGaLUkBBpFamlsU8BKiZGq8UVj0zj+IG1KUk01TTR9MTGaSuHBNFFjYzOm2pog0ZTiXxGbWgKModYC0gwqaNsomGqtkaY/GaSY0uXDXpe5gTvjDJ2ZOxf2l5zM2ufsve9aZ5+z7t7rzD2rci5zrs2wK5VK5ZylOuxKpVLpENrqsNUvqwcz40K/evUY9PkRtVVutbPpa2As+jmtzzNsVu9VLxvuMzOv5p5s8yd13VjrdrZkjs9+9Y/qFnXmGPS5Vp10aejU69VQL223Lq+XpnFrbAvbrVNleNr2S0d1BfBh4KqIOKHOpWReGEnbaRHxaqtjEbGV8p7aScdQNkfEp0fQfCMlK/MBdSqweDx1HSXHI+JKAPWHwGeBb46koTo1Ijrpp2mrgUeBTwLr2qvK6+bUuI2GDhyzc4Z2zrAvBo5GxAmAiDgaEc+qR9KRNd4r25fyOvW76iPAD3K2uaTRmdqnLm3MzNTu7GtKHp+pPqN2qW9VH1b3q7sbsyX1EvW3mSF+wwTa3Kcua7LlG+oT6g61kbDzIkpqNjKv5qGm87JJ3ak+rX5mHPQeDbuBt6VuP89zfLA5V546oK5X9wAr1OXqY+oBda96QVadl+P0dKamayvqLGAlcDPFYaNOUXvTxm3qQ2pPHluq/jrPwa8sWUkmNerCvCeeyO2a3L9K3aX+iPL+ZtQbcrz61XtyIlEZTyKiLRswC+gHngJ6gffm/iPA3JSXAX0prwP2AzOyfBtwR8oXA0+lvBa4O+UHgWtT/gRwb8o7gEUpXw3sTHkrJSU9wK3AwATZ3AcsSzmANSnf3mTL7cALlOwVtwDTm87LAUo2+7nAM8C8CR7Lgfw7Lc/557I8O//OoGTTmNNk48dTfgPwV2B5lt+Y/azN/d3AdOBvwPx2Xa+p2w3AfSk/BlwF9AAPUSY/b8kx6gG6ss6FTdff99upfwt7Tub12A/8LPfNbLq2FgG/S3kVcAy4JMvvAH4BdGW5t3Hv1G38traFRCJiQF0KvAe4Ftg8gtjz1og4nvIDwHbgq5SsDlta1N9MuVF2UWZEvTlLugbYoqfyMzSyFa9kMFflJuDO0do1HCO0+bXUG8qLz3+abddnuOGDwKcoS/NVWe/BPC/H1V3Au2jKFjQBzFD7U94N3Jfy59XrU55PcQDPUxxFI4PRYuCfEbEPICJeBsix2RERL2X5ELCA8oXULlYD30r5/ix3AVuivCLuuTz/UOy6HNietkwlV0iTiFYhkS7gbktWlJPA25uO7Y2Iwym/H1gK7Ev7ZjBETsPK2NHWt/VFiYP1AX2WVDk3Aa8yGKqZflqTY01t/6E+r76T4pRvafERW4GvqbMpF9dOSrb3F1tcqKe6PktzRsQQNg/bpKntX4DvqN8D/q3OOb3OEOXx5owbX10FfABYERH/ydBWYzxficEYqAyt74km+STtfeYyB3gfcLkaFAcclBVPyybAwYhYMUEqjhW3Af8CrqDch680HTvWJAtsjIgvTaBu5z1ti2Gri9VFTbuupCx7j1CcK/z/zOz3A18EuiPiD6cfjIgBYC9wF7AtSuz3ZeCw+rHUQ/WKbPIbMjYJrBm9VcMzjM3NTKEsqaHMpB/Nth9ycEmwiOLAXszyder0dCqrgH1jrftZ0A28kM76UuDdQ9T7MyVWvRxAvUCdjK/97aEkUl0QEQsjYj5wGDgKfDRj2W9mcNXzJHCh5UEz+exkSauOJxndlBXPa8CNlC+mVuwAetSLANTZ6oIJ0vG8pZ0PHWcBG9VD6u+Byyjx2DuAu9TdFKc0HD+mONgHhqmzmRJ73Ny0bw1ws3oAOAhcl/u/ANyq7qNcuGPNUDY3cwxYou6nzOjW5/4bgScz9LCJEudunJ+9wC+Bx4ENEfHsOOg+Wh4GpqWdGyi6nUFE/JeyQvp2jsd2zlxZTQZWc+Zs+ifAPODvlBj9PcAe4KW0qwe4M+3qp4TiJju9wE3q45RwyLFWlaI89P4K8EiO8XbKs6TKOFJ/mt7hWP4feyAivt5uXc5X1Fn5fGIO5ctzZUQ81269Kucek3HpWal0GtvUN1H+42VDddaV8aLOsCuVSqVDqO8SqVQqlQ6hOuxKpVLpEKrDrlQqlQ6hOuxKpVLpEKrDrlQqlQ7hf7bBsWDDoNoJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(train[['Survived', 'SibSp', 'Parch', 'Age', 'Fare']].corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
    "# 似乎只有Fare 与生存率相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eaae9940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探索生存 与 sibsp的关系 \n",
    "sns.factorplot(x='SibSp', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297ead124e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived 与 Parch 的关系 \n",
    "sns.factorplot(x='Parch', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 家庭人数少的生存机会较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age 与 Survived\n",
    "g = sns.FacetGrid(train, col='Survived')\n",
    "g = g.map(sns.distplot, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(train['Age'][(train['Survived']==0) & (train['Age'].notnull())], color='Red', shade=True)\n",
    "g = sns.kdeplot(train['Age'][(train['Survived']==1) & (train['Age'].notnull())], color='Blue', shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fare \n",
    "# df[df['Fare'].isnull()]\n",
    "df['Fare'].fillna(df['Fare'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "g = sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297eb26ceb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'] = df['Fare'].map(lambda i : np.log(i) if i > 1 else 0)\n",
    "sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 分类值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297eb2e1eb8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sex\n",
    "sns.barplot(x='Sex', y='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eadc8c88>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass \n",
    "sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eb1e6da0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass and Sex \n",
    "sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarked \n",
    "df['Embarked'] = df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eb1947f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked \n",
    "sns.factorplot(x='Embarked', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eaf0ff28>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked , Pclass \n",
    "sns.factorplot('Pclass', col='Embarked', data=df, kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297eac2e4e0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age | Sex, Parch, Pclass, SibSp \n",
    "sns.factorplot(y='Age', x='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Pclass', hue='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='SibSp', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Parch', data=df, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297eb4f4860>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Age', 'Sex', 'SibSp', 'Parch', 'Pclass']].corr(), cmap='BrBG', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充年龄缺失值 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_title = [i.split(',')[1].split('.')[0].strip() for i in df['Name']]\n",
    "df['Title'] = df_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297ec607668>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Title', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt              1\n",
       "Col               4\n",
       "Don               1\n",
       "Dona              1\n",
       "Dr                8\n",
       "Jonkheer          1\n",
       "Lady              1\n",
       "Major             2\n",
       "Master           61\n",
       "Miss            260\n",
       "Mlle              2\n",
       "Mme               1\n",
       "Mr              757\n",
       "Mrs             197\n",
       "Ms                2\n",
       "Rev               8\n",
       "Sir               1\n",
       "the Countess      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Title')['Title'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(['Capt','Major','Col','Don','Dona','Dr','Jonkheer','Mlle','Mme','Ms','Rev', 'the Countess'], 'Rare', inplace=True)\n",
    "df.replace(['Sir'], 'Mr', inplace=True)\n",
    "df.replace(['Lady'], 'Miss', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in df['Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297ec6c3550>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='Cabin', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297ec794f60>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y='Survived', x='Cabin', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ticket'] = pd.Series([t.replace('/', '').replace('.','').split(' ')[0] if len(t.split(' '))-1 else 'X' for t in df.Ticket])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fsize'] = df['SibSp'] + df['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297ec82dfd0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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flwEvBW5q+jmeB7x0eH0o8E9NP0eAZw2vnwZ8CfiZhn7eCnwC+HTj7/4u4Mjmv6+LgTcNr1cChzf2tQKYAZ4/gW3t8fcKbATOG16fB/zOpGqf5j2YcS5VeNKq6m9oPHenqu6tqn8cXj8E3Aoc1dBPVdV3h8WnDY+JfgOQZA3w74EPTXK7iy3Jsxn9Q/0wQFU9UlUPNnZ5MnB7VX39yW5oL3+vsy/ZuRj4j0+2n12mOWCOAu6etbyNhn+Yi2m42vwERnsXHdtfkeQG4D7gs1U16X7+B7ABeHzC252rgL9Kct1wmcmkvRC4H/joMNz7UJJnNvSzyxnAJY3b/8GquhdG/0MDnjupDU9zwIx1GcJykeRZwKeA36yq73T0UVWPVdVPMDrr+sQkPzapbSf5BeC+qrpuUtvch5Oq6qWMruT/jSQvm/D2D2Y0zPijqjoB+B6jocXEDSepngp8smP73aY5YMa5VGFZSPI0RuHy8ar6s+7+ht39zwMLXsz2BJwEnJrkLkbD1Z9P8qcT3P5uVXXP8Hwf8L8ZDZcnaRuwbdYe3uWMAqfDKcA/VtU3m7YP8M0kzwMYnu+b1IanOWDGuVThgDdMX/Fh4Naqem9jP6uSHD68fjrwSuCrk9p+Vb2zqtZU1QsY/S6urqqzJrX9XZI8M8mhu14DrwYm+g1fVc0Adyd58bDqZOCWSfYxy5n0Do/g+y/ZeSPwFxPbcueR9qV+AK9h9K3L7cD5TX1cAtwLPMro/2xnT3j7P8toaHcjcMPweE3Dz3E8cP3Qz03ABY2/l1fQ9C0So+MjXx4eNzf+3n8C2DL89/pz4Aca+ngG8M/AYRPc5h5/r8BzgKuArw3PR0yqPy8VkNRmmodIkpaYASOpjQEjqY0BI6mNASOpjQGjiUjy2Jyrf1+wj7Z/t3iVaSn5NbUmIsl3q+pZS12HDizuwahNkh8d5pi5IcmNSdYO6787PF84a49ne5KPDuvPmvW5Dw53CdUy5B6MJiLJY8BXhsU7q+o/JflD4ItV9fHhco0VVfXw3L2dJIcBfwv8GvAvjOYn+cWqejTJB4Zt/Mni/kSahM4br+mp5eEaXYk9298D5w/zwPxZVX1t7oeGa60+Dryvqq4b7kTxk8C1o7d4OhO8+E6Ly4BRm6r6RJIvMZpk6sokb6qqq+c0ezejK5M/OiwHuLiq3rmIpaqJx2DUJskLgTuq6g8YXbF7/Jz3fwF4FfCWWauvAk5P8tyhzRFJnr9IJWvCDBh1ei1w0zBL3o8Ac4+jvA3418CuA7oXVtUtwLsYzUh3I/BZRvMSaxnyIK+kNu7BSGpjwEhqY8BIamPASGpjwEhqY8BIamPASGrz/wDS1ZdryqcbmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Fsize', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex'] = df['Sex'].map({'female': 0, 'male': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "index_Nan_age = list(df['Age'][df['Age'].isnull()].index)\n",
    "for i in index_Nan_age:\n",
    "    age_med = df['Age'].median()\n",
    "    age_pred = df['Age'][((df['SibSp']==df.iloc[i]['SibSp']) & \\\n",
    "                            (df['Parch']==df.iloc[i]['Parch'])&\\\n",
    "                            (df['Pclass']==df.iloc[i]['Pclass']))].median()\n",
    "    if not np.isnan(age_pred):\n",
    "        df['Age'].iloc[i] = age_pred \n",
    "    else:\n",
    "        df['Age'].iloc[i] = age_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 14 columns):\n",
      "Age            1309 non-null float64\n",
      "Cabin          1309 non-null object\n",
      "Embarked       1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null int64\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "Title          1309 non-null object\n",
      "Fsize          1309 non-null int64\n",
      "dtypes: float64(3), int64(6), object(5)\n",
      "memory usage: 143.2+ KB\n",
      "['X' 'C' 'E' 'G' 'D' 'A' 'B' 'F' 'T']\n",
      "['S' 'C' 'Q']\n",
      "['A5' 'PC' 'STONO2' 'X' 'PP' 'CA' 'SCParis' 'SCA4' 'A4' 'SP' 'SOC' 'WC'\n",
      " 'SOTONOQ' 'WEP' 'STONO' 'C' 'SCPARIS' 'SOP' 'Fa' 'FCC' 'SWPP' 'SCOW'\n",
      " 'PPP' 'SC' 'SCAH' 'AS' 'SOPP' 'FC' 'SOTONO2' 'CASOTON' 'SCA3' 'STONOQ'\n",
      " 'AQ4' 'A' 'LP' 'AQ3']\n",
      "['Mr' 'Mrs' 'Miss' 'Master' 'Rare']\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "print(df.Cabin.unique())\n",
    "print(df.Embarked.unique())\n",
    "print(df.Ticket.unique())\n",
    "print(df.Title.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['Cabin', 'Embarked','Ticket', 'Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId', 'Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_X</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Rare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  Pclass  Sex  SibSp  Survived  Fsize  Cabin_A  \\\n",
       "0  22.0  1.981001      0       3    1      1       0.0      1        0   \n",
       "1  38.0  4.266662      0       1    0      1       1.0      1        0   \n",
       "2  26.0  2.070022      0       3    0      0       1.0      0        0   \n",
       "3  35.0  3.972177      0       1    0      1       1.0      1        0   \n",
       "4  35.0  2.085672      0       3    1      0       0.0      0        0   \n",
       "\n",
       "   Cabin_B     ...      Ticket_STONOQ  Ticket_SWPP  Ticket_WC  Ticket_WEP  \\\n",
       "0        0     ...                  0            0          0           0   \n",
       "1        0     ...                  0            0          0           0   \n",
       "2        0     ...                  0            0          0           0   \n",
       "3        0     ...                  0            0          0           0   \n",
       "4        0     ...                  0            0          0           0   \n",
       "\n",
       "   Ticket_X  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rare  \n",
       "0         0             0           0         1          0           0  \n",
       "1         0             0           0         0          1           0  \n",
       "2         0             0           1         0          0           0  \n",
       "3         1             0           0         0          1           0  \n",
       "4         1             0           0         1          0           0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "train = df[:train_len]\n",
    "test = df[train_len:]\n",
    "test.drop('Survived', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = train['Survived']\n",
    "X_train = train.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.8       , 0.86666667, 0.76404494, 0.87640449, 0.83146067,\n",
      "       0.7752809 , 0.80898876, 0.80898876, 0.87640449, 0.84090909]), array([0.73333333, 0.78888889, 0.76404494, 0.82022472, 0.82022472,\n",
      "       0.86516854, 0.83146067, 0.74157303, 0.83146067, 0.81818182]), array([0.72222222, 0.84444444, 0.75280899, 0.85393258, 0.80898876,\n",
      "       0.83146067, 0.80898876, 0.74157303, 0.85393258, 0.875     ]), array([0.77777778, 0.83333333, 0.78651685, 0.83146067, 0.79775281,\n",
      "       0.85393258, 0.79775281, 0.7752809 , 0.85393258, 0.82954545]), array([0.81111111, 0.81111111, 0.76404494, 0.83146067, 0.80898876,\n",
      "       0.85393258, 0.80898876, 0.76404494, 0.82022472, 0.84090909]), array([0.81111111, 0.82222222, 0.75280899, 0.88764045, 0.86516854,\n",
      "       0.80898876, 0.84269663, 0.78651685, 0.87640449, 0.85227273]), array([0.62222222, 0.61111111, 0.7752809 , 0.83146067, 0.83146067,\n",
      "       0.76404494, 0.80898876, 0.79775281, 0.86516854, 0.85227273]), array([0.72222222, 0.76666667, 0.75280899, 0.82022472, 0.79775281,\n",
      "       0.73033708, 0.84269663, 0.7752809 , 0.85393258, 0.82954545]), array([0.82222222, 0.84444444, 0.79775281, 0.87640449, 0.82022472,\n",
      "       0.75280899, 0.82022472, 0.80898876, 0.87640449, 0.86363636]), array([0.77777778, 0.81111111, 0.79775281, 0.88764045, 0.85393258,\n",
      "       0.79775281, 0.82022472, 0.75280899, 0.87640449, 0.86363636])]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Cross validation scores')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)\n",
    "random_state = 2 \n",
    "classifiers = []\n",
    "classifiers.append(SVC(random_state=random_state))\n",
    "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
    "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))\n",
    "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
    "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
    "classifiers.append(MLPClassifier(random_state=random_state))\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression(random_state=random_state))\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, X_train, y=Y_train, scoring='accuracy',cv=kfold, n_jobs=-1))\n",
    "\n",
    "print(cv_results)\n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_res = pd.DataFrame({'CrossValMeans':cv_means, 'CrossValerrors': cv_std, 'Algorithm':['SVC', 'DecisionTree', 'AdaBoost', 'RandomForest',\n",
    "                                                                                       'ExtraTrees', 'GradientBoosting', 'MultipleLayerPerceptron',\n",
    "                                                                                       'KNeighboors','LogisticRegression', 'LinearDiscriminantAnalysis']})\n",
    "g = sns.barplot('CrossValMeans', 'Algorithm', data=cv_res, palette='Set3', orient='h', **{'xerr': cv_std})\n",
    "g.set_xlabel('Mean Accuracy')\n",
    "g.set_title('Cross validation scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 160 candidates, totalling 1600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=-1)]: Done 199 tasks      | elapsed:   29.6s\n",
      "[Parallel(n_jobs=-1)]: Done 699 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1399 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1597 out of 1600 | elapsed:  3.4min remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed:  3.4min finished\n"
     ]
    }
   ],
   "source": [
    "# 调节hyperparameter 找到最合适的模型\n",
    "DTC = DecisionTreeClassifier()\n",
    "adaDTC = AdaBoostClassifier(DTC, random_state=7)\n",
    "ada_param_grid = {'base_estimator__criterion':['gini', 'entropy',],\n",
    "                'base_estimator__splitter': ['best', 'random'],\n",
    "                'algorithm': ['SAMME', 'SAMME.R'],\n",
    "                'n_estimators': [10,50],\n",
    "                'learning_rate':[0.0001,0.0003,0.001,0.003,0.01,0.03,0.1,0.3,1,3]\n",
    "                }\n",
    "\n",
    "adaDTC = GridSearchCV(adaDTC, param_grid=ada_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "adaDTC.fit(X_train, Y_train)\n",
    "ada_best = adaDTC.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305274971941639"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaDTC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   25.0s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8305274971941639"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调节 ExtraTrees 找到最好的模型 \n",
    "ExtC = ExtraTreesClassifier()\n",
    "\n",
    "# search grid for optimal parameters\n",
    "ex_param_grid = {\n",
    "    'max_depth': [None],\n",
    "    'max_features':[1,3,10],\n",
    "    'min_samples_split': [2,3,10],\n",
    "    'min_samples_leaf': [1,3,10],\n",
    "    'bootstrap': [False],\n",
    "    'n_estimators': [100,300],\n",
    "    'criterion':['gini']\n",
    "}\n",
    "\n",
    "gsExtC = GridSearchCV(ExtC, param_grid=ex_param_grid, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsExtC.fit(X_train, Y_train)\n",
    "ExtC_best = gsExtC.best_estimator_\n",
    "gsExtC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   28.1s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.835016835016835"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调节随机森林参数 找到最好的模型 \n",
    "RFC = RandomForestClassifier()\n",
    "\n",
    "rf_param_grid = {'max_depth':[None],\n",
    "                'max_features': [1,3,10],\n",
    "                'min_samples_split':[2,3,10],\n",
    "                'min_samples_leaf':[1,3,10],\n",
    "                'bootstrap':[False],\n",
    "                'n_estimators':[100,300],\n",
    "                'criterion':['gini']}\n",
    "gsRFC = GridSearchCV(RFC, param_grid=rf_param_grid, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsRFC.fit(X_train, Y_train)\n",
    "RFC_best = gsRFC.best_estimator_\n",
    "gsRFC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   11.4s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   29.7s\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:   59.0s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8237934904601572"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gradient boosting tunning\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {'loss': ['deviance'],\n",
    "                'n_estimators':[100, 200, 300],\n",
    "                'learning_rate':[0.1, 0.05, 0.01],\n",
    "                'max_depth':[4, 8],\n",
    "                'min_samples_leaf':[100, 150],\n",
    "                'max_features':[0.3, 0.1]}\n",
    "gsGBC = GridSearchCV(GBC, param_grid=gb_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "gsGBC.fit(X_train, Y_train)\n",
    "GBC_best = gsGBC.best_estimator_\n",
    "gsGBC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   16.5s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   56.7s\n",
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8361391694725028"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVC classifier \n",
    "SVMC = SVC(probability=True)\n",
    "svc_param_grid = {'kernel':['rbf'],\n",
    "                 'gamma': [0.001,0.01,0.1,1],\n",
    "                 'C':[1,10,50,100,200,300,1000]}\n",
    "gsSVMC = GridSearchCV(SVMC, param_grid=svc_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "gsSVMC.fit(X_train, Y_train)\n",
    "SVMC_best = gsSVMC.best_estimator_\n",
    "gsSVMC.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ5/N119/3dRm1USE/OOP59u9e8HjYdzkyc722CBKN/3L5Zdcwj+ee67OFDALFiygqKgovv7DH/4w6QBIg8HQckipkIjINBH5SkTWi0iNDIIi0kdE3hWRT0XkcxGZ4W6fLSIrE162iAx19y1y64zt65rKa4jxzKpnyX+wP547A+Q/2J9nVj17WPWpKud997tMmjCBb9auZfVnn/G7229np5u1N4aV6AU0E5YsXpx8hL6IIx7V84sl5Bhb8MQTFBUWxgXm0UcfrZGAsjlQW4oZg8FQk5QJiYh4gQeB6cAJwCwRqX7HuAV4QVWHARcBfwZQ1WdUdaiqDgUuATapamJ63tmx/aq6ixTzzKpnubLgJ2wu3oKibC7ewpUFPzksMXl30SL8fj9XXXllfNvQIUM4Zfx4Fi1ezOTTTuPiSy/lRDcT7x/vvZdBw4YxaNgw7v3TnwAnpcgZ55zDkJEjGTRsGM///e8AzL35Zk4YMoTBI0ZwvZvuPZG/PPQQN9x0U3x9wVNPce3PfgbAuRdcwIiTTmLg0KE8/OijSW3P7tgRcMTwmp/+lBOGDOGMc85xUte7ojLvzjsZNX48g0aM4Mqf/AS1bf7xwgus+PhjZn/vewwdOpSK/fuZNHEiK/7zH7Asnn3mGU488UTGjBkTT1MPkJ2dzc0338yQIUM46aST2LlzZw2bFi9eHJ8YbNiwYfF8XXfddRcnnngiQ4YMiWdDXrlyJSeddBKDBw/mvPPOY9++fQBMmjSJX/3qV0ycOJH77ruP3bt3c/755zNq1ChGjRrFhx9+2JCv1mBoc6SyjWQ0sF5VNwCIyHPAOUBiHEOBdu5yLlBETWYBh/f4Xw8/e/1nrNxRexr5/xQtI2RVzQBcHi3nB6/+iFE9HsWbZOT70K6DuXfqH2o955erVjFi2LBa9y9bvpwvP/mEfv368fEnn/DEU0+x9IMPUFXGjB/PxAkT2LBxIz179uTVV14BnDQme/fu5aVXXmHtF19USfeeyAUzZzJ2wgTuuvNOAJ7/+9+52b3JPv7ww3Ts2JGKigpGjRvH+eedR6dOnZLa+NIrr/DV11/zxSefsHPnTk4YOpTvX345ANf8+MfcevPNAFwyZw7/Kijgggsu4IGHHuLu+fMZOWKEU4nb/lK0eTM3zp3Lx0uW4MvM5PwLLuDlf/yDc887j7KyMk4aM4Y7fvtbbrjxRh555BFuueWWKrbcfffdPPjgg5x88smUlpYSCAR47bXXePnll1m6dCmZmZns3bsXgEsvvZT777+fiRMncuutt/Kb3/wmPmfL/v37Wbx4MQAXX3wx1113HePHj2fLli2cfvrprFmzptbvzGBoq6RSSPKArQnrhcCYamVuA94UkWuBLGBKknouxBGgRJ4QEQv4J/BbTZLnRUSuBK4EJ6fTokWLquzPzc2NP7WGw+HaQ0iqNUQkRsgKoapYSfJehS2LkjrycAWj0VrLlIfDjBgxgs49elASDPL24sXMOOMMbK8XgDPOPJO3Fi1iypQpvHXDDVx3ww1MmzaNcePGYUejpKWnc9kPf8jpp5/OtGnT4uewbJuSYJBATg59+vblnfff5+ijj2btV18xePhwSoJBfn/fffzrX/8CYGthIStXr2b0qFGoKqWhEOluXSXBIG8vWsR5M2dSHomQ07EjE045hYpwmJJgkIK33uLe++6joqKCffv2cUz//kyaMgXLtikLhSptUqUsEuG9pUs5efx4Arm5WLbN+TNn8va773Lq1KmkpaUxccIESoqLOWHAAN59911KiosrPzARRo4cyU9/+lO++93vcvbZZ5OXl0dBQQGzZs3CsixKSkrw+/0UFhayb98+hg8fTklJCeeffz6XXXYZJSUlWJbFWWedFf9dvPXWW3z55Zfx0xw4cICioqJ4+hbLsggGgzV+W82N0tLSZm9jjJZiq7GzKqkUkmRdeKrf8GcBC1T1DyIyFnhaRAapqg0gImOAclX9MuGY2aq6TURycITkEuCpGidSfRh4GJxcW9XzzaxZsyZ+Q/jz2X+u/SqCQfL/chybi2umke/brg+vfbfgkHJtjRg8mH/93/8lPTYzLY122dnxfeleL+k+X+W6z0fA52P4oEF8snQpBa+/zu23385pU6Zw6803s2LJEt7597957u9/57HHHuOtggJGnHQStirnnnUW8379a2ZfeCGvLlzI8ccdx8xzz6VdRgaLFi/m/ffeY+n775OZmcmkqVPx2jY5gQAiQnZ6etyGnECANK+XjLS0+Dafu+4HfnH99axYsoTevXtz2+23o5ZFTiCA1+MhK6Ge2HrA58Pv9ZITCDhil5ZGms9HTkYGfr+fdm725exAAFElJyFtPsCv585l5llnUfDGG0yZMoW333wTv89HRkZGlbxdtm0jIvFt2dnZeDwecnJy8Hq9dOnSJb5PVVm6dCkZGRlJv8OSkhICgQDD6vAsmwMtJS8UtBxbjZ1VSWVjeyHQO2G9FzVDVz8AXgBQ1Y+AANA5Yf9FVAtrqeo2970E+BtOCC2l3DFpHpm+qmnkM32Z3DHpENLIu/zX5MmEQiEeeeyx+LblK1aw+L33apSdcMopvLxwIeXl5ZSVlfHSK69wyvjxFBUVkZmZyfcuvpjrf/YzPvn0U0pLSzlw4AAzpk/n3rvvZuVnn+H1elm5fDkffvAB8379awBmnnsuLy9cyLMvvMCF3/kO4Dxxd2jfnszMTNauXct/3Glza2PCKafw3AsvYFkW27dv5103JBSbL6Vz586UlpbyjxdfjB+Tk52ddL6RMaNHs/j99/n222+xLItnn3+eiUlSz1cZWJnw+uabbzhx4EBuvO46Rg4bxtovv+S0yZN5/LHHKN+/H6JR9u7eTW5ODh06dOD9998H4Omnn2bixIlJr++0007jgQceiK8nm37YYDCk1iNZDvQXkX7ANhxRuLhamS3AqcACERmAIyS7AUTEA3wHmBArLCI+oL2qfisifuBM4O0UXgMAswc6aeRvXnQrW4q30qddb+6YNI/ZA2fVGb6qCxHhpRde4GfXX8/83/+eQCBAft++3Hv33Wwrqqq3w4cN4/JLLmH0yScD8MM5cxg2dChvvPkmv7zpJjweD36/n7/cfz8lJSWcc8EFBINBVJV7fv/7pOfv0KEDJwwYwOo1axjtpnufdvrp/O8jjzB4xAiOO/ZYThpTPRJZlfPOOYd/v/suJw4fzrH9+8dv/O3bt+eK73+fE4cPJ79vX0aNrEweevmll3LVNdeQkZHBRwmi2aNHD+68/XYmn3Yalm1z5vTpnHP22Q3+PO994AHeXbwYr9fLCQMGMH36dNLT01n5xReMHDuWtLQ0Zpx+Or+bN48nH36Yq669lvKKCo7q148nHn+8Mi1/QpT0T3/6E1dffTWDBw8mGo0yYcKE+NwrBkOzIHGMV+wVDjuvWM/JYBBSnKE8pWnk3e689wJe4HFVvUNE5gErVHWh24vrESAbJ+x1g6q+6R47CZivqicl1JcFvAf43TrfBn6uqnX2kW01aeQPk5Zi6xGzM1lafqg6N0zsu08y4NKkkW98WoqtR8TO2LxDyUQiccruRBI9dq+XRWvWMOmUUyBhZtKDoVmkkVfVAqCg2rZbE5ZXAyfXcuwi4KRq28qAEY1uqKFtUtto/NhTXrLyiSP5axMig6E+qguEbVcViUikMlN3Il5vZUg3Pb32h9wXX4T585lYVAS9esGdd8Ls2Sm7HJMixWCoTl0Ckzha353Fkg0bnNBBIOD8c/t8zsvtZWdoY1QXidgA3VioKRKp+juK/dZiIuH1QkbGoaccevFFuOEGqKhwejxt3Qqx8WopEpM2LSSqipj8UI3D/v2wfbvzT+L3Q48e0L59U1vVuMR+K+67gvOP7/c7o/fLy6u2tXi9lQITCFQKjJk0rOUSEwlVKCtzRCKxPSIaTT4NdmOJRF3s3w8bNzpTgldUVN1XXg4332yEpLEJBALs2bOHTp06GTE5XPbvh8LCyn+gSMRZh9YnJi6qyoEDBwh4vc7NIZn3EUsXc+AAuIMhUa0MS8Q8GK+3auLLhrwMjU9imCnmSSSGm2L7wFnftq1qD8IUikS8Lbu0FN24wRGMDRth00ZkwwbYuAlxMzTUypaaQxgaizYrJL169aKwsJDdu3fXXTASqfOfNxgOE6g2pqG5kjJbt29P3qbw7bfQrVtlu0IDaSmfaVl5OUN69Ki9gMcDya4jFhIrKXFE+FBIzG9WPedZ9ZdlOec6WLFq5oIVu7lqwvC06tvi61Y0LhTqNlJr2MkBpwmehLqXrCiIoCLg9aIe5x2fYKuNJco+v5OPzbZtsEFtRd1542zsyn0J6wC2uttc2+yYrW5ZqQji3bIF/+ZCfJu2kLalEP/mQtI2FeLbs7fKAL1I9y6E+/QiMmW8857fi27z/ohv956aH1ifPgf/ITeQNiskfr+ffv361V9w/fo6nzIWrVrFsIEDG9m61NCotu7YAe+9B++/78Rk68Lng65doXt359WjhyMwsfXYNnfQYUv5TBdt346/V6+DP1DECYf5/Yd+8lhDf+yVGHJJ7AQQE63t2w/tPAcjWMmmFTgIwbLVRlGC0aBzs7YtonaUsBWOv1tqxW/OiHN9allI9R5ObpuERiKIZaFqI7gdJEScg70CHi/q8SAeL0q1UdQxfYq6YXB3ryBE7Sh7gpUegFRbikU5pMqe2D4gHMa/tQj/pi34NxXi27wV3+YtzvuOaolbO3cimt+H0ORTKO/bm2h+H6J9+2D17YVWGywrQElIyb3lt3gShyZkZsIddzT0Wz9o2qyQGA6SsjL46KNK8Yilu+/UyRHa6jHZ2L6f/9wRndhr3Trn+CSDEmnXDrp3Z3B2NhxzTFWRiS137lxnd+w2w8F4DB4PZGcf2nkORrBq6cVm2RY2NpbajhCoTcSOErGjhO0IUbWIYqHiIVxRzpbVH7miJHg8PjxeHx6PD5/HS5rXhyQ2XsdsqTQYJBZqSncfAg/OI24IHikm05c840GcSATvtu34Nm2Jv7yuWHiLdjjiF/uM2udi5fchPGYU5f1cocjvTbRvb/Qgv7uKs6cDkPPHB/Bu34nm5eGZP9/02jI0AZYFn31WKRwff+z84wYCMHo0fPe7cMopcMIJ8PLL8V4icTIy4LbbYObM5PWXlTlPyYki4758GzY45929u2bILJl3k+jZxLbVktbEcJDUIliq6ngNrjBYqtiqhK0wEY0SsSNErChRjaLEnuYF50YPHp8Hj3jwEMAnHtLcEh5PiOy07EpRshSiFmg0dmLHHq8X/D6njakpsSy8RTvwbd6Cd9NWfJs249vkeBfewiIk4fdr52QT7duH8NATsc45g6grGNH8PmhuuzpOcvBUnD2dirOn88W6QsaPPYmsDqmdbcMIiaGSzZudG/h778GHHzqNxACDBsEVVzjCMXp0zVGyMbGYPx+KiqBnT5g7t3YRAWeA1DHHOK9qfLJqFZMGDnREZPfuqkKTKD5ff+3YWlpas/7c3JoCU118OnUy3k01EgXCEQklaked0JIdIWJHiNrOPnX/BA+xOJBXPIh48IoHv8dHwHMIN/rm1m3atvHs3OV4FZu34tu0hSGr19Fh1058W7chkUhl0cwMrL59iJxwHBXTpxLN7000vy9Wfh/sDu1T2+5kW47oRqOgMW9HjsjnaYSkLbN/vyMYMa9j82Zne8+eMH06TJgA48c7N9z6mDmzbuE4FLzeypt/XZSW1hSZxNdXX8GuXTW7Zfr9Vb2b2jycVuDdqGqC92A5YSbbcjwHVyQs2yKqlRN6JbYZeMWDR7x4REjz+vFIE3sCjY0qnm/3VIagYqKxeQvezVvxBCszgGt6OtqjO9H+RxE8dSJWrN0ivw92l86p76QQGzAbjYIVJf4t+XzOQ1779s5v2+eDb9emPD0KGCFpU0gkUrWd47PPnJtrdjaMG1fpdRx9dLPvsVOFWJtKEu8mTjRa07tJFJ+1a2HRIifkVp1avJtO4bDz+R2Kd+OOPG6wB1cLttrVQkyOQMS8h4gdJWSFWXdgQ40QkyB4RPCIF694SPP6CbQ2gUhEFc++/Xg3b8W3cXPcu4i1W3jKyiuL+n1Ee+Vh9etLaNyYygbu/N5Y3buxatOO85ggAAAgAElEQVQuBh5VzwNOY2BbEIkmtAW5bUCBdKdNsb4BsEfo39gISWtGtTL88957jF+yxMkd5vXC0KHw0586XsewYYfXg6gl4PM53kZd3XXB6QRQl3ezdq0jSLbNiYnHHYx3kzDyGHDGI9xwg7Psikl1cXAaqJ2wUtgOE3HDTbGupI73ICCKqDjtD26IySNCtv/Qci01FzIWvhZvPLZ6dKPk59fEG5WrI8UlbhhqC76NrlC47Rae4spOHur1YuX1INq3D+UjhyU0cPfB6tn9yA4cjfWui/U6i/mDMS8jI6PSy/D5mt2DnhGS1sauXY638d578MEHzs0PoF8/dkydSt655zreR7vGbdxrNeTkOK/+/WsvE43Crl18vGQJI7KyDs67ad/eCcVVnxO+ogL7prkcWLMS2wPqEdQd5KYeD/g8IF7E5yPd6yPg9eLxuk+hXg/qcd7xeNDEUdQeoePOA6Tt7ux0dfV63HLuci3HqbdykJ3G63KOrVL/EbihZSx8rUp3Vl/RDnJvuR3PtiKsvr3jnkUsJOXdVzk2R0WwenbH6tuHijNPJ5rfl6gbirLyekJaEzxAxbyMaJS4YHg8kJ7mtB3GvAy/v8W04RkhaelUVMDSpZWN5LGpYDt0cNo3JkxwXr16sW7VKvJawPiMZo/PBz17UjJgANT1eVbzbnTHDuzt2/A89dekEQcpLaPDnx9LsufwaEAL1yHjDNirJkweSRCgmqIVF6Yq+5xtI8MWmVkBV9ic/WnLPsYTqjpLqScYIveeygnprG5dieb3Jjh1siMSbptFtHde0/XsinkZVdKmKPjTnHEd6emV44laeNqclm19W8S24csvK4Vj+XInXUNaGowaBTfd5AjHoEEt5mmm1ZKTg52dRahfL8oi5RSHS4lqlH5vvYV/+84axa2e3dn17r/csRo22BbivmPZzrgDK7ZsuWWcbVX32fF0HmJZbNy2h37dcmscJwnpQCR2zoTjYnZUHuPak8Q2bLuyjmT7rGp1VLfbthHLRu0K5wYcDuOJnT+UfKprBXa/8ixW395oZhN3iIgNgownY1RHONPTHS8jMddaK/y/NELSEigsrBSODz6AWE6dAQPg8sth4kQYM6ZV9C5qDVi2RdAKURIppSRcigJeEdK96QQkndJfXFtj5LEdCFDy82sqx0h4vYCfakPtDon9HXcQPhINw43Aqg07ajRid510Br6iHTXKWj27Ex1w7JEyzSEufAplpcRbs9PSqgpGK/AyDoa2c6UtieJiWLKkUjw2bnS2d+sGp57qeBynnOI07hqaBRE7QkUkSHGkhHKrAhR8Hh+ZvowaSUGrjzyur/G4rVPy82tqF95UEvMyEgfFejyuWHihR89W7WUcDEZImgORCHz6aaVwrFzp/HgzM+Gkk+CyyxzxOPbYZtdbo62i6gzG2xfaz4FwCWErDAJpkka2r/4eUrGRx4b6SbnwxrI0R6NV062kpztdyxO9jFgX28K98dxwBiMkTYMqfPNNZe+qJUucnjweDwwZAldf7QjHiBHJs8camgRbbUJWiLJIeVw8vq3YS7o3rcV3r23uNIrwVhnIV62LbVpa5bgMr9cRDfPQ1mCMkBwp9uxx2jdiXkdRkbO9b18491xHOE4+uWXP3xEMOt4VVP4TxrOtVluuPh96YtbY6mWbkFh7R3G4hNJIGTaKTzyke9PxiIcsv3kqbZZU8TJswP1tpaU53bsTu9g2t5QsLZCUComITAPuA7zAo6o6v9r+PsCTQHu3zFxVLRCRfGAN8JVb9D+qepV7zAhgAZCBMx/8T1WTpBxtaoJBWLas0uv48ktne26uIxjXXuuIR35+k5rZKMRmi0tPd+aH9nqrZotNtlx9EqHEZVWnW3OymeYSz5koNIlzniSKUvW05g0g1t5xIFJMhRWss73D0MTU8DJcYgP5qo/+Nt9fSkiZkIiIF3gQmAoUAstFZKGqrk4odgvwgqr+RUROwBGGfHffN6o6NEnVfwGuBP7jlp8GvJaaqzgIbBtWr64UjmXLHDHx+50Q1S9/6QjHkCGt6wkoGnWm8ezUqfGSIG7a5KQ7SZbCvLbl2kQpFveOzVcRI0GI1LYJa5TyaAX7w8WENYp4PKR508j2pjnlbAFst5OOtPnG1UMmMe185cbKN6Vqavhk79XThaSnO15GYhfb1vQ/1gJIpUcyGlivqhsAROQ54BwgUUgUiA2xzgWK6qpQRHoA7VT1I3f9KeBcmkpItm+n+5tvwp//7IStvv3W2X7ssU7u/wkTYOxYp1tgayTmNfTunZprbOxZ+hJEybaihCJBSsMlFFfsJ2pF8Uga6dKDHDyVwpMoSvFxEgnrZWVUTW+YZDnZRE5xTynRvgQ7qbZc37a69iW1M/GkjbmtHmICnCjEiZ4kOLZ7YiFQb9VjY9MUN+N0IW2RVApJHrA1Yb0QGFOtzG3AmyJyLZAFTEnY109EPgWKgVtU9X23zsJqdeYlO7mIXInjudCtWzcWLVp0UMZ3ffttjnr0UdJ37SLUpQsb5sxhz9ixtP/8czp88gkdPvmErC1bOB4Id+jAvmHD2Dt8OPuGDyfcuXNlRZs2HdR5U0lpMMiiVasapzLbdv6pfT4nV1QjUlpaetDfV0NJzGEVz04hh+ZdBKPKqj1BqozwUJKvJ26P3+yrlU28Kde4N9a1L2Fjkn3BqM2q3eV1HFtHffVsOvTyyQlGLFZtP3DwBx5hgmVBVi1vpP+lFBIqD7H0w6WH/BtvKKkUkmQ/o+qPULOABar6BxEZCzwtIoOA7UAfVd3jtom8LCIDG1ins1H1YeBhgJEjR+qkSZMabvkzz8A99zghGyCwaxcn/P73lU+0gUC8W+7yvDxGnXkm3UTo1vAzNAmLYvN8HA6RiOOJdO3qpGFJwdPgokWLOKjvqw4iVoSKSAUHQgcoj5QjCD6vj3Rv+mG3d6xavoqBo5p/ypmWYie0HFtbip1fLPuCMSePISsttVGRVApJIdA7Yb0XNUNXP8Bp40BVPxKRANBZVXcBIXf7xyLyDXCsW2fiJNnJ6jx8br45LiJxYunWH33USUXi5vgvW7Wq7bjW5eXOtfbt22xH0as7S195pJz9wf2ErTCCkOZLIyc9p6nNMxhaJakUkuVAfxHpB2wDLgIurlZmC3AqsEBEBgABYLeIdAH2qqolIkcB/YENqrpXREpE5CRgKXApcH+jW75lS/LtZWXOiPK2hm0741xycpzR9c0s9YOtNqFoiNJwKcWhYqJ2FI94SPelk+Mz4mEwpJqU3RFUNSoi1wBv4HTtfVxVV4nIPGCFqi4EfgE8IiLX4YSoLldVFZEJwDwRiQIWcJWq7nWr/jGV3X9fIxUN7X36VM4WmEjPno1+qmZPOOz0PuvWzRnj0ky8L8u2CEaDFIeKKQmXoKr4PD7SfelkSPP0lgyG1kpKHy1VtQCni27itlsTllcDJyc57p/AP2upcwUwqHEtrcYdd8CVV1YNb2VkOLPYtSXKy50G9fz8IzJdZ31Ub+8A8Hv9ZPmzzPgOg6EJaV4xiubC7NnO+803O2Guw5gKtUUSC2Xl5jqN6k3UJ9+0dxgMLQMjJLUxe7bzWr/e8UbayhNvKOSEs3r0cEYFH+HrttVGVdlVtoviUDGWbZn2DoOhmWOExFBJWZnjfeTnH9FZ5SzboiJaQUmohBI3GWJJqISAL5Dy/u8Gg+HwMUJicEZpl5U540K6dDki6T/CVjje3lERqQAq2zs8Hg8ZftNgbjC0FIyQtHWCQScfVV6e0703RagqIStEeaScA8EDpr3DYGhFGCFpq8Qy9qalOaGsFMx7YqtNMBqMj+8w7R0GQ+vECElbJBUZe2NV29H4+I7ScGl8fIdp7zAYWi9GSNoasYy9vXo5KV8OAlXFUgvLtuLvoWiIkBUibIWJ2lEnGSJmfIfB0JYwQtJWiM3JkZYG3bs7abhrFHGEImpHsWznPWyFCVthQlaIqB1FVR1xcDPnesWLRzz4PD7SvGlGOAyGNogRkrZALJTl9RLp0Q0Li2g4RNSKxr2JiBUhqlE347kjFoLgEQ9ejxe/x0/A1/Sj2w0GQ/PDCEkrIh5yUgtLbSJ2hFDZAULRMJEuHQlhsfHApsr5lhC8Hi9e8ZLmSyMgRigMhtbAi2teZP4H8ykqKaLX5724c8qdzD5xdsrOZ4SkhaCq2GoT1SiW2li25YSd7AhhO0LEjmDjTtaEMy2sp7wCT2Y23l69yEgL4PHsIDvt4NpFDAZDy+LFNS9yw1s3UBF1xmdtLd7Klf93JUDKxMQISTMh3pAd8yYsRxxCdpiwHSVqRVB3Di9V8HgED4JHvHjFQ4Y3UNk+EY1CKAhdezWrjL0Gw6GS+ITdM6cnc8fPZeaAlpP7LjbVQcgKEYqGCEaDlctWsHJbkjJVyiYpEzs+Vnbjvo1YalU5f3mknJvfudkISUsnNsWrE36ynYZsO+K2UbjtE+Dc9N2pWL3iwSs+fOIhzZfRsIbsYNB575nXbCefMhgOhupP2NtKtnHDWzcAHJSYxG7mNW7MCTf0+I064caceLOPbdu5YycZuzLi64n1JB6XuO1w8IiHgC9AujeddF86AV+AgDdAus9Zz/Rn0jGjI+m+dNbvXZ+0ji0HaplnqREwQtJIxIQiakfj7RNhO+I2ZEeJaqzHkwfQyoZs8eL3+Ah4DjO3lW07DerZ2U6akybK2GswNBZRO8rOsp3MWzwvLiIxKqIV3Pj2jby94e26n94TbuRhK3xY9vg8vviN3Gt7yQ5lV7m556Tl0Dmzc5Wbfbo3vYYApPvS4+sZvoz4cvUyMaEI+AL4PA2/VY9+ZDTbSrbV2N4nt89hXX9dGCFpINUbsp0usU4bxfoDG6u0Tyh2vFusVzykef0EJIVJECMRCIeclO85OSaUZWj2qCp7K/ZSVFJEUUkR20q2UVRSxJrNayj9ppSikiJ2lu6sEaJJpDxSzhe7vojfrAO+ALnpufGn9PhNvNpNOX7jTihT42afZDnxZt6c52yfO35uFQ8OINOfyR2n3pGycxohqYftFbsoDVlo/N4sIIoHT3ykdpX2iSON262XvF7NYvIpgwGgNFxaKRLFjkgUlVYuby/ZTtAKVjkm3ZtOJ38n8rvkM673OPJy8uiZ05O7PryLPRV7apwjLyeP9+e8f6QuqcUQC/fFe221M722mpyySDkZWbl4PMlDRQJNIyK2BeUVzpwhnTqZUJbhiBG2wmwv2V7Dm0h8HQgdqHKMRzx0zepKXk4eg7oO4rSjT4sLRc+cnuS1y6NTRidWr1hd40k/059Z4wk7w5fB3PFtbMbSg2DmgJnMHDCTL5Z9wfhTxpOVlpXS86VUSERkGnAfzpztj6rq/Gr7+wBPAu3dMnNVtUBEpgLzgTQgDPxSVf/tHrMI6AHEflWnqequlF4HzSxUFA5DJOzMo57CjL2GtoetNrvKdiUXiWLHq9hdtjvegzBGh0AH8trl0Tu3N2PyxpDXLkEkcvLomtUVv7dmNoWGUP0JuyX22mrtpExIRMQLPAhMBQqB5SKy0J2nPcYtwAuq+hcROQFnfvd84FvgLFUtEpFBwBtAXsJxs92529sWqk4oKy0NevU+opNPGVo+qsqB0IGa4pAgGDtKdxCxI1WOy/RnxgVhQJcB8eUeOT0cwcjumfL5Y2JP2IbmSSo9ktHAelXdACAizwHnAIlCokA7dzkXKAJQ1U8TyqwCAiKSrqqH14euJRMLZeXmNnrGXkProCJSEReE7SXb48tfF37NgVUHKCopojxSXuUYn8dHj+we5OXkMarnKMeLaNezijeRm55rcqgZ6kRUtf5Sh1KxyAXANFX9obt+CTBGVa9JKNMDeBPoAGQBU1T14yT1XKWqU9z1RUAnwAL+CfxWk1yEiFwJXAnQrVu3Ec8999whXUeovARPHe0PwVCUQHqKm5pUnZfPD95DF5BgWZBAVvNvkG8Ldr6z6x2e2PQEu0O76ZLehTn5czi166m1lo/aUfaE97A7tJvdod3sCu2qsVwcLa5xXEd/Rzr7O9M1oytd0rvQNdCVLmld4ssd/B2aVXr/tvDdH0kqyirIzs4+5O948uTJH6vqyPrKpfIOmOwRpvoNfxawQFX/ICJjgadFZJCqk4tcRAYC/wOclnDMbFXdJiI5OEJyCfBUjROpPgw8DDBy5EidNGnSIV3E+k/eJiOrPVKLB7Bqww4GHtX9kOqul1goKz3d6dp7mJNPNecui4m0djtfXPMif/roT/HG412hXdz3zX3YHW2O63xcjbaJbSXb2FW2K56iP0Zuei49c3qS3zWfcTnj4h5EzJvont2ddF96i/k8ofV/90eaL5Z9wZiTx7ToxvZCoHfCei/c0FUCPwCmAajqRyISADoDu0SkF/AScKmqfhM7QFW3ue8lIvI3nBBaDSFp8ViWM3dIx45OmhMTymo13Pn+nTUG2AWjQX7/0e/j6wFvIN4GMaHvBHpm96zSgN0zp6fJm2ZoNqRSSJYD/UWkH7ANuAi4uFqZLcCpwAIRGQAEgN0i0h54FbhJVT+MFRYRH9BeVb8VET9wJvB2Cq+haQgGHW+kZ0/IzGxqawyNwL6Kfby54U0K1hVQVFr9eaqS12e/Tl67PDoEOph2CUOLIWVCoqpREbkGp8eVF3hcVVeJyDxghaouBH4BPCIi1+GEvS5XVXWPOwb4fyLy/9wqTwPKgDdcEfHiiMgjqbqGI05sHvXMDOjSNenkU4aWw+6y3bz+zesUrCtgydYlRO0oeTl5ZPuzKY2U1iifl5PHid1ObAJLDYbDI6WtxKpagNOlN3HbrQnLq4GTkxz3W+C3tVQ7ojFtbDZEo44n0qmTydjbgikqKeK1da9RsK6ApduWoij92vfjqhFXMaP/DAZ3G8xLa18yA+wMrQozsr05UFHhCEeeydjbEtm8fzMF6wp4dd2rfLrD6bl+fKfjue6k65jRfwbHdz6+SpjKDLAztDaMkDQlsYy9WVlOxl6f+TpaCuv2rOOZLc+wYu0KVu1eBcDgboOZO34uM/rP4OgOR9d5vBlgZ2hNmDtXUxGJQCgEnTs7gwxNKKtZo6qs2r2KV9e9SsG6gvicDyN7juTWibcy45gZ9M7tXU8tBkPrxAhJU1BR4XTn7WUy9jZnbLX5dPunFKwr4LX1r7H5wGY84uGkXicxZ+gcjio9ignjJzS1mQZDk2OE5EgSC2Xl5DieiMnY2+ywbItl25ZRsK6AgvUF7Cjdgd/jZ3yf8Vwz+hpOP/p0OmV2ApxBaQaDwQjJkSOWsddMPtXsiFgRlmxdwqvrXuWNb97g2/JvCXgDTMyfyE3jb2LqUVPJDeQ2tZkGQ7PFCMmRoLzcaUg3GXubDcFokPc2v0fBugLe+uYt9of2k+XP4tSjTmX6MdM5td+pKU8rYTC0FoyQpBKTsbdZUR4p598b/03BugLe3vA2ZZEyctNzmXr0VM7ofwan9Dkl5enQDYbWSIOFRETGA/1V9QkR6QJkq+rG1JnWwgmFnEGG3btDtsmJ1FQUh4p5e8PbFKwr4N1N7xKMBumU0Ylzjz+XGf1nMK73ONK8h5cM02Bo6zRISETk18BI4DjgCcAP/JUko9LbPIkZe3v0OOyMvYaDZ2/FXt5Y/wYF6wt4f/P7ROwI3bO6M2vQLGb0n8HovNH4PMYZNxgai4b+N50HDAM+AXBnLjRzvFYnlrG3QwfnZUJZR4ydpTt5bb2TmuQ/hf/BUove7Xrz/WHfZ0b/GQzvMbxZzbthMLQmGiokYTeZogKIiGmFrE4w6HTv7dHDGaluSDmFxYVON911BawoWoGiHN3haH4y6iec0f8MBnUdZDLoGgxHgIYKyQsi8hDQXkSuAL5Pa8q6e7iUlTo5skzG3pSzYd+GuHh8tvMzAAZ0HsAvxv6CGf1ncGynY414GAxHmAYJiareLSJTgWKcdpJbVfWtlFrWErAsxwvp0NEJZZkbWKOjqny156u4eKz5dg0AQ7sN5Vfjf8WM/jPo16FfE1tpMLRt6hUSEfECb7hzphvxiBGbfMrvd2YxNDQaqsrnOz+P57XasG8DgjA6bzS/mfQbph8znbx2eU1tpsFgcKlXSFTVEpFyEclV1QNHwqhmjW1DRTlkuhl7v/2qqS1qFdhq8/H2jylYV8ArX77Czg924hUv43qP44rhVzDtmGl0zera1GYaDIYkNLSNJAh8ISJv4cxSCICq/ndKrGquxCafMhl7G4WoHWVp4VIK1hXw+vrX2VG2gzRvGsNyh3HjxBuZevRUOmYYb89gaO40VEhedV9tFzP5VKMQtsJ8sOUDCtYV8MY3b7C3Yi8BX4D/yv8vZvSfwalHncrWz7cycNDApjbVYDA0kIY2tj8pImnAse6mr1Q1kjqzmhmlpY4HYjL2HhIVkQoWb17Mq+te5e0Nb1McKiY7LZsp/aYwo/8MJvebTKY/s6nNNBgMh0hDR7ZPAp4ENgEC9BaRy1T1vXqOmwbcB3iBR1V1frX9fdx627tl5rrzvCMiNwE/ACzgv1X1jYbU2eikpUH7LtCunQll1cGLa16sMnXsdSddR2ZaJgXrCvj3xn9THimnfXp7ph0zjRn9Z3BKn1MI+MxcLAZDa6Choa0/AKep6lcAInIs8CwworYD3N5eDwJTgUJguYgsVNXVCcVuAV5Q1b+IyAlAAZDvLl8EDAR6Am+756QBdTYuPXqAP9OISB28uOZFbnjrBiqiFQBsK9nG9W9dD0DnzM7MHDCTM/qfwdheY/F7zTgbg6G10VAh8cdEBEBVvxaR+u4Io4H1qroBQESeA84BEm/6CrRzl3OBInf5HOA5VQ0BG0VkvVsfDaizcTECUi+3v3d7XEQS6ZzZmU+u/ASvx4QDDYbWTEOFZIWIPAY87a7PBj6u55g8YGvCeiEwplqZ24A3ReRaIAuYknDsf6odGxs4UF+dAIjIlcCVAN26dWPRokX1mJuckBWqM0dTsCzYYmbKa0xbo3aUD/d8yMKihewq25W0zJ7yPaz9eO1B191SPlNjZ+PTUmxtKXaGykMs/XBpyvPMNVRIfgxcDfw3ThvJe8Cf6zkm2aO8VlufBSxQ1T+IyFjgaREZVMexyT6N6nU6G1UfBh4GGDlypE6aNKkec5Ozfu96MnwZtabdWLV8FQNHtYweRo1h687Snfzti7/x18//yo6yHfTJ7UNuei4HQjWHGPXM6XlI52spn6mxs/FpKbY2dztVFVttVi1fxZiTx6R8kraGCokPuE9V/wjx9o/6pvorBHonrPeiMnQV4wfANABV/UhEAkDneo6tr05DI6OqLC9azoKVC3h13atE7SiT8yfzP1P/h8n5k3nlq1eqtJEAZPgymDt+bhNabTC0DGI3fUVRVRR3XZ1n5Ng6ivOIHXuHqtuoXPZ4PHjw4BHPEZkyoaFneAcn7FTqrmcAbwLj6jhmOdBfRPoB23Aazy+uVmYLcCqwQEQGAAFgN7AQ+JuI/BGnsb0/sAznY6qvTkMjURGp4KW1L/HEyidYvXs1uem5zBk6h0uHXMpRHY6Kl5s5YCZAlV5bc8fPjW83GFoLsRu8qhKxIg266SvqRDTcbaqKIFVv+uLBK1484om/fB5flXWvx4sgiAiC4BFPfFnEXXeXY2zxbiHdl/rpvRsqJAFVjYkIqloqInV2/FfVqIhcA7yB01X3cVVdJSLzgBWquhD4BfCIiFyH8zFfrs43skpEXsBpRI8CV6uqBZCszoO5YEP9bNy3kac+f4rnv3yeA6EDDOg8gLum3MV5A86rdbzHzAEzjXAYmhXxm777pJ/41B/f79zpkz7V13bT94rTeaS2G77P46tyg2/oTb8l01AhKROR4ar6CYCIjARqdtOphjsmpKDatlsTlldTyyyLqnoHcEdD6jQcPrbavLvxXRasXMC7m97F6/Eyo/8M5gydw6ieo1rND97QslBVLLWwbAtLLWzbTnqDr/7Uj4LXU/UJ3+/1J33qr+0Gn0wAYmz2bqZ3bu/azG5zNFRIfgb8XUSKcL6qnsCFKbPKcMTYV7GP51c9z1OfPcXmA5vpmtWVn4/9ObNPnE237G5NbZ6hDRATCcu2sNWmNOQGPwQEIc2bRsAXIN2XTpo37aBv+obUU6eQiMgoYKuqLheR44EfATOB14GNR8A+Q4r4cteXLFi5gJfWvkQwGmRM3hhuHH8j04+ZTprXzDNvaDxstauIRSy8FPMofB4f6d50sv3Z+D1+euX2witefB6fGYPUQqjPI3mIyrEdY4FfAdcCQ3G61l6QOtMMjU3EjvDy2pdZsHIBy4uWk+HL4PwB53PZ0MsY2KX5dmU0NG+ShZ8SQ00ej4d0bzoBX4CALxAXCJ/Hh1e8VbwHj3hM3rUWSH1C4lXVve7yhcDDqvpP4J8isjK1phkai+0l23nmi2dY8PEC9kX2kd8+n19P/DXfHfhd2gfaN7V5hhZAokdhqVWlYVoQ/B5/lfBTokeR6sFwhqanXiEREZ+qRnG66V55EMcamhBVZem2pTyx8gleW/cattqM7jiaaydey8T8ieaf21CFesNP4iPdl06WP4t0bzo+r8+Enwxx6hODZ4HFIvItTi+t9wFE5BjAzJbYDCkLl/Hi2hdZ8OkC1u5ZS/v09lwx/AouHXIppetKGdjPhLDaIrX1fnJ2Vg0/pXvTnR5OtYSfDIbq1CkkqnqHiLwD9ADe1FgHbCdVybWpNs7QcL7Z9w1PrnySF1a9QEm4hEFdB/GH0/7AOcedQ4bfmYhrFWbITWsmWfgpNkCuPFIeDz+ledNI96XjFW9cLIyHajgcGjJn+3+SbPs6NeYYDgbLtnhn4zssWLmAxZsX4/f4OfPYM7l86OWM6DHCPEW2MlSVqB2tEn5KbKtIDD+ledPi4ya2e7fTv1P/JrXd0Lox7RwtkL0Ve3nuy+d46rOn2Fq8le7Z3fnluF8y+8TZdMnq0tTmGQ6RxPBTrM0icfCdx4j19G0AABhKSURBVOMhzZtGls8RijRvmgk/GZoFRkhaEJ/v/JwFKxfwytpXCFpBxvYayy0TbuH0o083E0a1MFSVkBUiYkXiAiAIPo8vHn5K86ZV6Sprwk+G5ooRkmZOKBri1XWv8sTKJ/hk+ydk+jP5zsDvcPnQyzm+8/FNbZ7hIAlFQ4StMIKQnZ5Nt6xu+Dy+uFAYr8LQEjFC0kzZVrKNv37+V575/Bn2VOzhqA5HMW/SPL4z8Du0S29XfwWGZkMoGiJiR0AhKy2LLlldyPBlmG6zhlaDEZJmhKqyZOsSFqxcwBvfvIGiTDlqCnOGzmF8n/EmtNGCCFthwlYYVSXLb8TD0LoxQtIMKA2X8o/V/+DJz57k6z1f0yHQgatGXsUlgy8xGUZbEBErQigaQlEy/Bl0z+pOZlrmEZlYyGBoSswvvAlZv3c9C1Yu4O+r/05puJQh3YZwz+n3cPZxZxPwBZraPEMDqCEe2UY8DG0P82s/wkTtKG9veJsnVj7BB1s+IM2bxlnHnsWcoXMY1mNYU5tnaABRO0ooGnKSE6rSLbsbmf5M03PO0GYxQnKE2FO+h799+Tee/uxptpVsi09HO2vQLDpndm5q8wz1EBcPtUn3ptM1qyuFvkLyO+Q3tWkGQ5NjhCTFfLr9UxZ8toCFXy0kbIUZ32c8v5n0G6YePdWEP5o5lm0RjAax1cbv9dM5szNZaVnx+VoE01XXYIAUC4mITAPuw5lf/VFVnV9t/z3AZHc1E+iqqu1FZDJwT0LR44GLVPVlEVkATKQyaeTlqtqsUtoHo0H+7+v/Y8GnC1i5cyVZ/iwuHnQxlw29jGM7HdvU5hnqICYeqorP46NTZicn460vvalNMxiaLSkTEhHxAg8CU4FCYLmILHTnaQdAVa9LKH8tMMzd/i7O5FmISEdgPfBmQvW/VNV/pMr2Q6WwuJCnP3uav335N/ZW7KV/x/7c8V93cP6A88lJz2lq8wy1YKtNMBrEsi38Hj8dMzqSnZbtTOtqBggaDPWSSo9kNLBeVTcAiMhzwDnA/2/v3qPkrOs7jr+/u7P3ey67uWxuIEWI0QALqKAmUCSiR6zmaNIcBAuNNzyKp9ZYeihSPcXWitJyxLQIKJRFI0hKoUjBaNGAIRggCQKRaxKu5UCyt5ndmW//eH4TJpvZbLIzszOz+3mds2ef5ze/eeYzO5fvPs8z8/ttH6H/SuDvsrQvB+50976CpMyRu/PrZ3/NdVuu4+6n7gbgzCPP5LzF53HKnFP0RlSiUp4iPhRnKDVErCJGa00rjTWN1FTW6DETOUz25sjwed6w2XJgmbtfENbPAU529wuz9J0H3A90unty2GX3At9x99vD+nVE0/7GgXuANe4ez7LN1YSJuDo6Ok7o7u4e0/2IJ+NZvwjYO9TL3S/dzW27bmNXfBctVS2cNeMsPjjjg7TXto/ptgptoHeA2obS/1hxIXOmPBWNmGvsG0Z9rOc6enp6aGxszG/AAiiXnFA+WSdLzqVLl252967R+hVyjyTbq3OkqrUCWJeliMwEFgF3ZTR/DXgRqCaaN/6rwGUH3JD72nA5XV1dvmTJksOMH9nx2g7qYnX7/kt9/NXHue7h61i3fR19g328temtXLn0Sj70Jx8q+ePo2zZtY+GJpT+xVT5zujsDQwMMpYaosApaaltoqm6iNlab857Hhg0bGOvzajyVS04on6zKub9CFpKdQObXsjuB3SP0XQF8Pkv7x4Fb3X0w3eDuL4TFuJldC/xVHrIe4MZHb+Tiey7muTeeY1bTLJYduYztr25n486N1FTWcPZbz+a8d5xH7PkYC48t/TfnyWR48WiuaaapJioeGmZGJP8KWUg2AUeZ2QJgF1Gx+PPhnczsaKAN2JhlGyuJ9kAy+8909xcs+nfyI8DWfAe/8dEbWf2fq+kbjE7L7Nq7i2u2XENbbRsXv+diVrxtBVPqpgCw7XnNOlgKModlr7AKmqqbaK5tVvEQGQcFKyTuPmRmFxIdlqoEfuju28zsMuBBd18fuq4Eun3YyRozm0+0R/OrYZu+0cymEx062wJ8Jt/ZL77n4n1FJFN9VT2fO/Fz+b45GSN3J5FMMJiMdljTw7LXVdWpeIiMo4J+j8Td7wDuGNZ2ybD1S0e47jPA7Cztp+UvYXbPvfFc1vbde0c6MifjaficHu0N7dTGajWyrkiR6KvVWcxtmcuzbzx7QPuspllFSCOgYdlFSpn2/7P45unfpL6qfr+2ulgda05dU6REk1MimaAn0UNPooeYxZjZOJMjpxxJZ0snjdWNKiIiJUJ7JFmsWrQKYL9Pba05dQ0fPeajRU428Q0mB0mlUuyN79WcHiJlQq/OEaxatIpVi1Yd8D0Syb/B5OC+w1Y1sRpilTGOaDtCw7KLlAkVEimKbMOyp+f0eNqeVhERKSMqJDJuModlr66sPmBYdhEpTyokUlCjzekhIuVPhUTyLplKEk/G9w3Lrjk9RCY2FRLJm6HUEP2D/VRVVNFW20ZDdYOGZReZBFRIJC/6B/txdzqbO6mvqlfxEJlEVEgkJylP0ZvopbG6kY7GDn3fQ2QS0qtexiyRTBAfitPe0E5rbav2QkQmKRUSOWzuTt9gH5UVlcxrnUdtrPRnXRSRwlEhkcOSTCXpTfTSVtfG9IbpGq5dRFRI5ND1D/aT8hSzm2fTVNNU7DgiUiJUSGRU7k5Poof6qnpmNM7Q8CUish8VEjmoRDLBwNAA7Q3ttNW26YS6iBxAhURG1Jfoo8IqmN86XyfURWREKiRygGQqSU+iJzqhXj9dE0iJyEEV9CM3ZrbMzB43sx1mdsD0gmZ2hZltCT9PmNnrGZclMy5bn9G+wMweMLMnzexmM9Pof3k0MDRA/2A/nc2dzGicoSIiIqMq2B6JmVUCVwFnADuBTWa23t23p/u4+0UZ/b8AHJexiX53X5xl098CrnD3bjO7Gjgf+H4h7sNkkj6hXhurpbOtU6PzisghK+QeyUnADnd/yt0TQDdw9kH6rwRuOtgGLTrTexqwLjRdD3wkD1kntcHkIHvje5laN5W5LXNVRETksJi7F2bDZsuBZe5+QVg/BzjZ3S/M0ncecD/Q6e7J0DYEbAGGgMvd/edmNg24393fEvrMAe5097dl2eZqYDVAR0fHCd3d3WO6H/Fk/KBfuhvoHaC2oTxORGfLmvIUANUV1SXziayenh4aGxuLHWNUypl/5ZJ1suRcunTpZnfvGq1fIU+2Z3tXGqlqrQDWpYtIMNfdd5vZEcC9ZvYosOdQt+nua4G1AF1dXb5kyZJDDp5ptDnbt23axsITF45p2+MtM2vKU/QkemipaaG9ob2kzoVs2LCBsT5e40k5869csirn/gp5aGsnMCdjvRPYPULfFQw7rOXuu8Pvp4ANROdPXgVazSxdAA+2TRnBwNAAvYleZjXO0gl1EclZIQvJJuCo8CmraqJisX54JzM7GmgDNma0tZlZTVieBpwCbPfoONwvgeWh67nAbQW8DxNOT6KHCipY0LaA5trmkjmcJSLlq2CFxN2HgAuBu4DHgJ+4+zYzu8zMPpzRdSXQ7fufrDkGeNDMHiYqHJdnfNrrq8CXzWwHMBW4plD3YSIZSg2RTCWZUjuFua06oS4i+VPQLyS6+x3AHcPaLhm2fmmW6/0WWDTCNp8i+kSYHKK+wT5wqK6sZlrDtGLHEZEJRmOAT2ApT7FnYA91sTrmt83XkO8iUhAaImWCig/FSSQTzGyaSXONzoWISOGokEww7k5vopfqymrmt86nJlZT7EgiMsGpkEwgQ6kh+gb7mFo3lan1U3UoS0TGhQrJBJGevXBO8xwaqhuKHUdEJhEVkjKX8hS9iV7NXigiRaNCUsbSsxd2NHTQWtuqE+oiUhQqJGWqN9FLZUWlZi8UkaJTISkzyVSS3kQvbXVtTKufpnGyRKToVEjKSP9gP8lUktnNs2mqaSp2HBERQIWkLKRnL6yvqmdGi06oi0hpUSEpcYPJQfoH+2lvbKettk0n1EWk5KiQlLC+RB8VVsG81nnUVdUVO46ISFYqJCUomUrSO9hbkrMXiogMp0JSYgaGBhhKDjGrcRbNtc3FjiMiMioVkhLh7vQO9lJTWUNnW6cmnhKRsqFCUgLSJ9Sn1U9jSv0UDbYoImVFhaTI+gf7AZjbOpf6qvoipxEROXwF/dfXzJaZ2eNmtsPM1mS5/Aoz2xJ+njCz10P7YjPbaGbbzOwRM/tExnWuM7OnM663uJD3oVBSnmJvfC91sTrmtc5TERGRslWwPRIzqwSuAs4AdgKbzGy9u29P93H3izL6fwE4Lqz2AZ909yfNbBaw2czucvfXw+Vfcfd1hcpeaPGhOIOpQWY0ztDshSJS9gq5R3ISsMPdn3L3BNANnH2Q/iuBmwDc/Ql3fzIs7wZeBqYXMOu4SM9eaBjzWubRUtuiIiIiZc/cvTAbNlsOLHP3C8L6OcDJ7n5hlr7zgPuBTndPDrvsJOB6YKG7p8zsOuBdQBy4B1jj7vEs21wNrAbo6Og4obu7e0z3I56MH/Tk90DvALUNo4++6+6kPEWsIkasojinpnp6emhsbCzKbR8O5cyvcskJ5ZN1suRcunTpZnfvGq1fId/Rsv2rPVLVWgGsy1JEZgI/Bs5191Ro/hrwIlANrAW+Clx2wA25rw2X09XV5UuWLBnDXYAdr+2gLlY34p7Dtk3bWHjiwoNuo3+wH3dnVvOsop4L2bBhA2P9O4wn5cyvcskJ5ZNVOfdXyENbO4E5GeudwO4R+q4gHNZKM7Nm4L+Av3X3+9Pt7v6CR+LAtUSH0EpSylPsGdhDbayW+W3zdUJdRCakQhaSTcBRZrbAzKqJisX64Z3M7GigDdiY0VYN3Ar8yN1/Oqz/zPDbgI8AWwt2D3IQH4rTm+ilo7GDWU2zinY4S0Sk0Ar27ubuQ2Z2IXAXUAn80N23mdllwIPuni4qK4Fu3/9kzceB9wJTzey80Haeu28BbjSz6USHzrYAnynUfRiL9An1qsoq5rfOpyZWU+xIIiIFVdB/k939DuCOYW2XDFu/NMv1bgBuGGGbp+UxYl4NpYboS/TRVtfG9Ibp+oa6iEwKOt6SJ/2D/aQ8RWdzJ401pf9pDhGRfFEhyYO98b3R7IWNmr1QRCYfFZIcJJIJkqkk7Q3ttNa26suFIjIpqZCMUW+il0qrpCZWQ1tdW7HjiIgUjc4GH6ZkKsmegT001zQzr3UelvV7lyIik4f2SA5D/2A/yVSS2c2zaappKnYcEZGSoEJyCBynJ95DXVUdc1rmaPZCEZEMKiSHYG98Lx2NHbTVtumEuojIMCoko2iubqappom6qrpiRxERKUkqJKNob2wvdgQRkZKmT22JiEhOVEhERCQnKiQiIpITFRIREcmJComIiOREhURERHKiQiIiIjlRIRERkZzY/lOlT0xm9grwbIE2Pw14tUDbzrdyyaqc+VUuOaF8sk6WnPPcffponSZFISkkM3vQ3buKneNQlEtW5cyvcskJ5ZNVOfenQ1siIpITFRIREcmJCknu1hY7wGEol6zKmV/lkhPKJ6tyZtA5EhERyYn2SEREJCcqJCIikhMVklGY2Q/N7GUz25rRNsXM7jazJ8PvttBuZnalme0ws0fM7PhxzDnHzH5pZo+Z2TYz+2IpZjWzWjP7nZk9HHJ+PbQvMLMHQs6bzaw6tNeE9R3h8vnjkTMjb6WZ/d7Mbi/xnM+Y2aNmtsXMHgxtJfXYh9tuNbN1ZvaH8Fx9V6nlNLOjw98x/bPHzL5UajnDbV8UXkdbzeym8Poa/+eou+vnID/Ae4Hjga0Zbf8IrAnLa4BvheWzgDsBA94JPDCOOWcCx4flJuAJ4NhSyxpurzEsVwEPhNv/CbAitF8NfDYsfw64OiyvAG4e58f/y8B/ALeH9VLN+QwwbVhbST324bavBy4Iy9VAaynmzMhbCbwIzCu1nMBs4GmgLuO5eV4xnqPj+qCU6w8wn/0LyePAzLA8E3g8LP8AWJmtXxEy3wacUcpZgXrgIeBkom/fxkL7u4C7wvJdwLvCciz0s3HK1wncA5wG3B7eKEouZ7jNZziwkJTUYw80hzc+K+Wcw7K9H/hNKeYkKiTPA1PCc+524MxiPEd1aGtsOtz9BYDwOz2xe/qBTdsZ2sZV2GU9jui//ZLLGg4XbQFeBu4G/gi87u5DWbLsyxkufwOYOh45ge8Cfw2kwvrUEs0J4MAvzGyzma0ObaX22B8BvAJcGw4X/ruZNZRgzkwrgJvCcknldPddwLeB54AXiJ5zmynCc1SFJL8sS9u4fr7azBqBnwFfcvc9B+uapW1csrp70t0XE/3HfxJwzEGyFCWnmX0IeNndN2c2HyRLsR/7U9z9eOADwOfN7L0H6VusrDGiw8Tfd/fjgF6iQ0QjKerfNJxb+DDw09G6Zmkbj+doG3A2sACYBTQQPf4jZSlYThWSsXnJzGYChN8vh/adwJyMfp3A7vEKZWZVREXkRne/pZSzArj768AGouPKrWYWy5JlX85weQvw2jjEOwX4sJk9A3QTHd76bgnmBMDdd4ffLwO3EhXoUnvsdwI73f2BsL6OqLCUWs60DwAPuftLYb3Ucv4p8LS7v+Lug8AtwLspwnNUhWRs1gPnhuVzic5HpNs/GT7F8U7gjfSucKGZmQHXAI+5+3dKNauZTTez1rBcR/RieAz4JbB8hJzp/MuBez0c5C0kd/+au3e6+3yiwxv3uvuqUssJYGYNZtaUXiY6rr+VEnvs3f1F4HkzOzo0nQ5sL7WcGVby5mGtdJ5Syvkc8E4zqw+v//Tfc/yfo+N54qocf4ieSC8Ag0QV/Xyi44r3AE+G31NCXwOuIjrm/yjQNY45TyXaTX0E2BJ+ziq1rMDbgd+HnFuBS0L7EcDvgB1EhxJqQnttWN8RLj+iCM+BJbz5qa2SyxkyPRx+tgEXh/aSeuzDbS8GHgyP/8+BthLNWQ/8H9CS0VaKOb8O/CG8ln4M1BTjOaohUkREJCc6tCUiIjlRIRERkZyokIiISE5USEREJCcqJCIikhMVEpkQzGxqxmitL5rZroz16kPcxrUZ33EYqc/nzWxVflKXBjO7z8wWFzuHlC99/FcmHDO7FOhx928Pazei53wq6xUnKTO7D7jQ3bcUO4uUJ+2RyIRmZm8JczVcTTTS8EwzW2tmD4Z5HC7J6HufmS02s5iZvW5ml1s0b8pGM2sPfb5hZl/K6H+5RfOrPG5m7w7tDWb2s3Ddm8JtHfAfv5mdaGa/CgMt3mlmHWZWFdZPDX3+yd6cs+XrZrYpfX9CYUzn+I6Z/a+ZbTezLjO71aL5KC7N+DtsM7MfWzRvyU/CyALDM30g3N+HLJq7oiEjx3aL5tv4Vl4fJCl7KiQyGRwLXOPux3k0Yuoad+8C3gGcYWbHZrlOC/Ard38HsBH4ixG2be5+EvAVIF2UvgC8GK57OdFIzPtfyawG+B7wMXc/AbgB+HuPxkz6FLDWzN5PNMbXN8LVvufuJwKLQr5lGZvsd/f3EA2T83PgM6Hf6vSQNOHvcJW7LwIGgE8Py9RONIji6R4NAPkI8EUz6yAaJWGhu78d+IcR/hYySamQyGTwR3fflLG+0sweItpDOYboDXa4fne/MyxvJpqTJptbsvQ5lWigR9w9PWzJcMcAC4H/sWhI/TWEAfXc/ZFw/duAT4XiAnC6mf2OaCiU94Xrp60Pvx8FHnX3l9x9gGieks5w2dPufn9YviHkzPRuor/Fb0OmVeE+vUY0lP6/mdmfEY3aK7JPbPQuImVv3xufmR0FfBE4yd1fN7MbiMYgGi6RsZxk5NdKPEufbMN1D2fAI2EvIpu3Ec0XkT6kVg/8K9EsmLvM7BvDcqdzpDKW0+vpXMNPiA5fN+C/3f2cA8KadRFNlLYC+CzRwJAigPZIZPJpBvYCeywaCvzMAtzGfcDHAcxsEdn3eLYDs83spNCv2swWhuVPAI1Eg0VeZWbNQB1RUXjVopF+PzaGXAvM7MSwvDLkzPRb4H1mdkTI0WBmR4Xba3b324GLyHKoTiY37ZHIZPMQ0Zv4VuAp4DcFuI1/AX5kZo+E29tKtHexj7vHzWw5cGV4o44B/2xmrxCdE1kS9jx+AFzh7ueb2fVhW88SzX55uLYBf2lm1xCNGLt2WKaXzOx84OaMj0z/DdAP3BLO61QQzWMvso8+/iuSZxZNGhRz94FwKO0XwFH+5vSnxcj0FmCdRzNTiuSV9khE8q8RuCcUFAM+XcwiIlJo2iMREZGc6GS7iIjkRIVERERyokIiIiI5USEREZGcqJCIiEhO/h8I0rie0Y1s6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(0.1,1,5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    plt.fill_between(train_sizes, train_scores_mean-train_scores_std, train_scores_mean+train_scores_std, alpha=0.1, color='r')\n",
    "    plt.fill_between(train_sizes, test_scores_mean-test_scores_std, test_scores_mean+test_scores_std, alpha=0.1, color='g')\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training score')\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
    "    plt.legend(loc='best')\n",
    "    return plt \n",
    "\n",
    "g = plot_learning_curve(gsRFC.best_estimator_, 'RF mearning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsExtC.best_estimator_, 'ExtraTrees learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsSVMC.best_estimator_, 'SVC learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(adaDTC.best_estimator_, 'AdaBoost learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsGBC.best_estimator_, 'GradientBoosting learning curves', X_train, Y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ensemble modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "votingC = VotingClassifier(estimators=[('rfc', RFC_best), ('extc', ExtC_best),\n",
    "                                     ('svc', SVMC_best), ('adac', ada_best),\n",
    "                                     ('gbc',GBC_best)], voting='soft', n_jobs=4)\n",
    "votingC = votingC.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "test_survived = pd.Series(votingC.predict(test), name='Survived')\n",
    "results = pd.concat([IDtest, test_survived], axis=1)\n",
    "results.to_csv('ensemble_python_voting.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cv_result in cv_results:\n",
    "    print(cv_result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(2,10):\n",
    "#     for j in range(2, 10):\n",
    "rf = RandomForestClassifier(random_state=2, n_estimators=500, min_samples_split=7, min_samples_leaf=3)\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=False, random_state=2)\n",
    "scores = model_selection.cross_val_score(rf, X_train, Y_train, cv=kf)\n",
    "#     print(scores)\n",
    "# print(i, j, scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 150 candidates, totalling 1500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   23.1s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 796 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1246 tasks      | elapsed:  6.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:  8.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)\n",
    "rf = RandomForestClassifier()\n",
    "rf_params = {\n",
    "    'max_features':['auto', 'sqrt', 0.33],\n",
    "    'min_samples_split':[2,3,5,7,10],\n",
    "    'min_samples_leaf':[1,3,5,7,10],\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini']}\n",
    "gsrf = GridSearchCV(rf, param_grid=rf_params, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsrf.fit(X_train, Y_train)\n",
    "gsrf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383838383838383"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsrf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_survived = gsrf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission['Survived'] = test_survived.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission_08_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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